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ESP "Smart Flow" Integrates Quality and Control Data for Diagnostics and Optimization in Real Time A. Al-Jasmi, H. Nasr, and H. K. Goel, Kuwait Oil Company and G. Moricca, G.A. Carvajal, J. Dhar, M. Querales, M.A. Villamizar, A.S. Cullick*, J.A. Rodriguez, G. Velasquez, Z. Yong, F. Bermudez, and J. Kain, Halliburton * Now with Berry Petroleum Company

Copyright 2013, Society of Petroleum Engineers This paper was prepared for presentation at the 2013 SPE Digital Energy Conference and Exhibition held in The Woodlands, Texas, USA, 5–7 March 2013. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract Intelligent digital oilfield (iDOF) operations include the transfer, monitoring, visualization, analysis, and interpretation of real-time data. Enabling this process requires a significant investment to upgrade surface, subsurface, and well instrumentation and also the installation of a sophisticated infrastructure for data transmission and visualization. Once upgraded, the system then has the capability to transfer massive quantities of data, converting it into real information at the right time.

The transformation of raw data into information is achieved through intelligent, automated work processes, which are referred to here as "smart flows," which assist engineers in their daily well surveillance activities, helping make them more productive and improve decision making. A major oil and gas operator in the Middle East has invested in such an infrastructure and is developing a set of smart flows for key activities and work flows for its production operations, with the ultimate goal of improved asset performance.

The project includes a smart flow to monitor, diagnose, and optimize wells that include electric submersible pumps (ESP); 50% of the wells in this field use ESPs. The ESP smart flow includes leading-edge technologies, such as: variable speed drive controller, subsurface equipment and sensors, advanced diagnostics based on artificial-intelligence agents, analysis of sensors signals, and automatically identifying ESP optimum operating conditions.

Using a steady-state nodal-analysis model combined with an artificial intelligent technique, the ESP smart flow is designed to provide rapid diagnostics and optimization in real time, generating actions, such as decreasing and increasing the pump frequency and choke setting. The ultimate benefit is to detect the signals that foresee unexpected well downtime and predict ESP system pump failures. The paper describes the main functionalities of the ESP smart flow as a powerful optimization tool that is capable of providing an interactive monitoring system that can assist operations personnel in managing ESP-operated wells. Introduction The iDOF project is being conducted in the Mauddud reservoir of the Sabriyah field, which is produced mainly with the assistance of ESPs. In many cases, because pump performance declines gradually, operators may not even be aware of problems until it is too late to recommend remedial action. Frequent problems are identified such as pump wear, solids-plugged intake, tubing leaks, viscous fluids, and gas interference. Gas interference is a common and repetitive problem. When flowing bottomhole pressure depletes below the bubblepoint pressure, gas is liberated at wellbore conditions, trapping gas in the pump causing low pump efficiencies and, in some cases, actually gas locks the pump. Typically, operators resolve this issue by reducing the choke setting. However, when the pump situation becomes critical because of excessive free gas buildup in the ESP stages, the operator may need to shut down a well for a period of several days or even months to allow the reservoir pressure to build up in the reservoir drainage area associated with the well.

When monitoring ESP performance in real time, operators can detect problems and act proactively to prevent those problems. To use this data to be able to act proactively and prevent problems requires specific work processes to transform the data into actionable information. Reducing pump problems can be achieved by acquiring the most adequate operational parameters and using that data to increase production efficiency. Data transformation into information can be more easily achieved with an automated work process.

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To restore production, prevent damage to the equipment, minimize expensive down-time with rig intervention, and avoid catastrophic pump failures, the operator worked with a service company to develop an automated workflow for ESPs that can increase the uptime production, extend the pump lifespan, and ultimately increase the oil recovery factor.

This automated, intelligent ESP work flow, called a "smart flow," is being implemented as part of a smart surveillance philosophy (Al-Abbasi et al. 2013a). This paper discusses how the ESP smart flow works. KwIDF Project This smart surveillance philosophy is being applied to the Kuwait Intelligent Digital Oil-Field (KwIDF) pilot project. KwIDF provides a platform to increase effectiveness through automating work processes and to shorten observation-to-action cycle time. Dashti et al. (2012) list the challenges that the KwIDF project was designed to address, which include: minimize production shortfall, increase staff productivity, minimize delays to bring equipment back online, reduce delay time in response to alerts and alarms and, most importantly, enable collaboration across different disciplines and allow seamless open discussion.

Al-Abbasi et al. (2013a) present the fundamental philosophy and practices of the new generation in workflow automation used in the KwIDF project. The full process provides: data integration and federation, applications, workflow orchestration, and the visualization services required to enable the deployment of the smart flows. Al-Abbasi et al. (2013a) also present the project's five essential components, which are: real-time data acquisition including but not limited to, pressure, temperature, and volumetrics; improved process and engineering procedures; integrated production models; artificial intelligent components; and iterative and intuitive graphical interfaces. Production Operations Smart Flows: First Generation of Workflow Automation. There are nine core KwIDF smart flows, which focus on well surveillance production operations for short-term decisions using real-time data to improve well performance, reduce well downtime, and improve field performance. Fig. 2 shows the nine KwIDF core smart flows, which are listed below and each described in Al-Abbasi et al. (2013a). This paper discusses the development and logic of the ESP smart flow.

1. Key Performance Monitoring (KPM) 2. Well Performance Evaluation (WPE) (For details, see Al-Jasmi et

al. 2013b.) 3. Smart Production Surveillance (SPS) (For details, see Al-Jasmi et

al. 2013a.) 4. Production Losses (PL) 5. Reservoir Visualization and Analysis (RVA) (For details, see Al-

Jasmi et al. 2013e.) 6. Electric Submersible Pump Diagnostic and Optimization (ESP) (the

subject of the current paper). 7. Production Allocation (PA) 8. Gas Lift Optimization (GL) 9. Reporting and Distribution (R&D) (For details, see Al-Jasmi et al.

2013d.) KwIDF ESP Smart Flow Scope The KwIDF Sabriyah digital oilfield has been equipped with a smart flow customized to monitor, diagnose, and optimize, in real time, ESPs under available production conditions. This smart flow, using downhole and surface real-time ESP operational parameters, enables production engineers to interactively monitor, diagnose, and optimize the ESP-operated wells. ESP smart flow functionality includes:

Monitoring alarm setting and data trending. Automated ESP well model diagnostics capability by nodal analysis. ESP system malfunctioning recognition by fuzzy logic analysis. Automated ESP operational parameters optimization.

The ESP smart flow comprises four major steps (Fig. 1): monitoring, diagnostic, advanced analysis, and optimization, all

in real time.

Fig. 1—ESP smart flow scope.

Monitoring in Real TimePump & Well Diagnostic 

(Nodal Analysis)Advanced Analysis  

(Fuzzy Logic)Optimization in Real Time (Nodal Analysis)

Fig. 2—Nine key KwIDF smart flows. The ESP smart flow is discussed in this paper.

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Data QC: Data Filtering and Conditioning Data filtering and conditioning is a powerful function integrated in ESP smart flow that provides data quality and control before diagnostic and optimization models. This functionality receives the real-time, raw data of tubing head pressure (THP), tubing head temperature (THT), pump pressure discharge (PPD), pump intake pressure (PIP), motor temperature (Mt), amperage, frequency, liquid rate, water cut, and gas/oil ratio (GOR). The functionality has a series of algorithms that automatically clean, eliminate spikes, detect frozen data, and estimate the average and standard deviation of the data. The following functions (see Fig. 2) are described as: Rate-of-change (ROC), Range checks (Range), Freeze checks (Freeze), Mean and standard deviation (SPC), Filtering (Filt) and Stability check. The filtering application allows cleaning up of the data and takes an average value for a specific period of time.

The objective of this function is to determine whether or not a signal has potentially "gone bad," even if there is a valid value in the input. If an event is detected, the algorithms provide an alarm and temporarily replace the bad value with a good value. Fig. 2 shows an example of how the raw data (liquid rate, water cut, pressure and temperature) are transformed and cleaned up to provide consistent and accurate data to improve the accuracy of the well model calculations.

A dedicated user interface runs automatically every day and delivers the quality-controlled raw fluid rate data of the last twenty hours. Based on the QC of the raw data, the daily average fluid parameters are calculated. Fig. 2 shows how the interface displays the results of data filtering and conditioning. The red X means that the data has not passed the relevant QC check. Regardless of the outcome of the QC process, the user has the freedom to accept or reject the final result.

 

Fig. 2—ESP smart flow raw data filtering and conditioning.

How KwIDF ESP Smart Flow Works This smart flow operates with real-time surface production data (pressures, temperatures, fluid rates, choke setting, WC, GOR, amperage, voltage, and frequency) and real-time downhole pump sensor data, such as pressure intake, pressure discharge, and motor temperature.

The ESP smart flow has these capabilities: ESP wells visualization dashboard. A map showing well status and all relevant real-time production data; one data

value every minute is available for visualization and plotting. Alarm system. If the upper or lower limit is exceeded, the smart alarm system provides an alarm message. Consolidated daily parameters. As a result of a filtering-conditioning and consolidation (averaging) process, a set of

volumetric data (such as liquid rate, oil rate, and water cut) and electric data (frequency, amperage) are generated on a daily basis.

Well model updating. Based on the daily consolidated data, the well models are updated and automatically matched on a daily basis.

Troubleshooting. Based on the wellhead and bottomhole data, the advanced diagnostics system identifies possible malfunctioning of an ESP system.

Production system optimization. Based on the wellhead and bottomhole data, the optimization module provides a set of ESP operational parameters from which the engineer can choose the most appropriate operating point.

 

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ESP System Diagnostics in Real Time Real-time monitoring is the most effective way to identify the degradation of the production system and prevent undesired events. Optimizing ESP run-life is essential to avoid additional workovers and maintain a suitable production level. The ESP smart flow is equipped with a robust and effective data-trending module that helps to identify both production and functional deviations. The available diagnostic and optimization tools are listed here.

Smart alarm system. The ESP smart flow is equipped with a smart alarm system that provides real-time alarms when the pumps are operating out of the pressure, temperature, and electrical limitations or they exceed the drawdown limitations. Generally, alarms are triggered when parameters are above or below preset thresholds. However, if the actual system flow condition is not also considered, this basic detection can generate false alarms. For example, when frequency or tubing head pressure is changed deliberately, then the flowing BHP changes, which is a normal event. The ESP smart alarm system can take into account such context modification. The ESP smart alarm system conducts real-time analysis on the data stream and makes recommendations about how to operate the well.

Smart diagnostics tools. The ESP smart flow is equipped with two diagnostic tools. One uses both the evaluation of the ESP system’s operational parameters and the ESP well model validation; the other provides the ESP system malfunctioning identification.

ESP system’s operational parameters evaluation. All ESP wells in the Sabriyah field are equipped with down-hole gauges providing real-time data relevant to the near-well and ESP pump performance. By tracking down-hole real-time operational parameters, deviation from established trends can be recognized and actions to improve production and extend the pump life can be taken. The down-hole gauges provide this standard information: pump intake pressure (PIP), pump discharge pressure (PDP), pump intake temperature (PIT), and motor oil temperature (MT).

ESP well model update and tuning in real time. The well model tuning is performed on-line daily, by comparing the calculated data (provided by the last available well test model) with the measured data. The well model tuning is required to:

Generate a virtual metered fluid rate, to be used if the measured value is not available (For more information about virtual metering, see Al-Jasmi et al. 2013a).

Perform a reliable sensitivity analysis, used for production system optimization.

An offline pre-setup nodal analysis model is already calibrated with the latest information from the production well test, pump data, well trajectory, PVT table, and the completion scheme. Using the latest production test, a multiphase flow correlation is selected to tune the model.

The real-time system automatically updates the nodal analysis model with the daily consolidated pressure/temperature data such as frequency, THP, PIP, THT, PDP, PIT and MT. The model calculates the gradient plot across the well pump and compares the calculated PIP, PDP and THP with those data from real-time sensors. Both calculated and sensor data are displayed in an ESP gradient plot (Fig. 3). Note that gradient line is displayed in blue, sensor signals in yellow, and the previous value in red.

Fig. 3—ESP smart flow real-time diagnostic. The real-time pump gradient plot (blue) is shown with real-time sensors (surface and down hole) (yellow).

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Based on the PIP, the system estimates the flowing bottomhole pressure (FBHP) at the middle of the perforation intervals. The system performs many sensitivity analyses, changing hidden properties (such as pump wear factor, multiphase correlation friction factor parameter, multiphase correlation gravitational parameter, and productivity index) until it gets a better match between THPCal-THPSensor, PIPCal-PIPSensor and PDPCal-PDPSensor. Under this condition the system reports the final liquid rate, FBHP, and water cut; again calculated liquid rate is compared to measured liquid rate.

When the system cannot find a solution, a quick diagnostic is provided based on the following criteria: Unmatched FBHP: Review multiphase flow correlation and/or friction and gravitational parameters and tuning. Unmatched pump ∆P (PDP - PIP): Problem with fluid viscosity, review behavior of the GOR, fluid PVT, and/or

pump wear factor. Unmatched liquid rate: Check the productivity index calculation.

ESP System Identification Events by Fuzzy Logic

The ESP smart flow is equipped with an advanced diagnostic interface using a fuzzy logic rules-based algorithm to determine the probabilities of the occurrence of five ESP signals, including: liquid rate, pressure intake, pressure discharge, amperage, and motor temperature. It also provides recommendations and displays an engineer's comments for an incident. Fuzzy Logic Basic Concepts. As the complexity of a system increases, it becomes more difficult and eventually impossible to make a precise statement about any particular behavior. "Fuzzy" means not clear, distinct or precise, not crisp (well defined), blurred (with unclear outline). Zadeh (1968) explains that fuzzy logic can be defined as “A form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their contexts.” In other words, fuzzy logic is the way the human brain works, and we can mimic this in machines so they will perform somewhat like humans. However, fuzzy logic should not be confused with artificial intelligence, where the goal is for machines to perform exactly like humans. Fuzzy logic deals with the concept of partial truth, where the truth value may range between completely true and completely false. Whereas, classical logic expects that everything can be expressed in binary terms (yes or no, hot or cold, high or low), fuzzy logic looks at the degrees of truth (It is hot, but how hot? It is high, but how high?).

The necessity for using fuzzy logic in petroleum engineering comes from the extensive uncertainties in the hydrocarbon production processes, and the engineers’ need to implement their thinking processes and evaluation approaches into automated workflows or simulation tools. The fuzzy logic in the ESP smart flow was designed to provide a prediction of five particular pump conditions that include: pump wear, plugging intake, gas interference, tubing leak, and viscous fluids problems.

The technology to implement fuzzy logic includes: Sensor infrastructure in place that collects the signals for intake pressure, discharge pressure, motor temperature,

liquids flow rate, and current (Al-Abbasi et al. 2013a). A data-processing infrastructure that allows filtering and conditioning of the data from the sensors (Al-Abbasi et al.

2013a). A predefined set of “conditions” that affect the performance of ESP systems and that are analyzed using this model

developed progressively over time. A predefined set of “rules” that characterizes each of the conditions and allows the calculation of the degrees of truth

for each of them. For predictive capabilities, the system calculates a slope for 90, 60, 30, 15, and 7 days back to the current day.

The smart flow determines trends and tendencies by calculating a slope for a predetermined period of time on the

different sensors' signals and compares those sets of trends to pre-established sets of rules called “Condition Models.” The closeness of the measured trends to the models is expressed as “Condition Match Indexes” (CMI), which represent the degree of truth that each condition has of developing based on a specific time period. Expert Rules. Rules are used to formulate the conditional statements that comprise fuzzy logic (e.g., if the motor temperature exceeds a certain limit value, the pump can burn). Conventional rule-based expert systems use human expert knowledge to solve problems that normally would require human intelligence. The fuzzy logic in the KwIDF solution was built on a set of worldwide expert rules and converts these rules to their mathematical equivalents. Table 1 shows the expected behavior of the signal when each of the conditions is developing.

 Table 1— Expected Trend of Monitored Signal when the Conditions are Developing

Signal PIP

Current (Amps)

MT PDP Liquid Rate Condition

Pump Wear + − − − −

Plugged Intake + − + − −

Gas Interference − − + − −

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Tubing Leak + − NC − −

Viscous Fluids NC + + − −

 For example, if “pump wear” is occurring, it is expected that intake pressure will increase, as shown by the “+” sign, but

the amperage will decrease (“−” sign). For a “tubing leak” condition, the motor temperature is not expected to change, so an “NC” is shown, and so on. Different conditions may trigger the same trend on a specific signal, and they may also be developing simultaneously so the ESP system continuously monitors the CMIs for each condition. The conditions that have the higher indexes are the most “true” and most likely to occur so must gain the user’s attention. Weight Factors. A weight or contribution factor is assigned to each of the signals being analyzed for each condition. Because it is a percentage or proportion of a unit value, the sum of all the contributions for each condition adds up to one. The arbitrary assigned "weight" represents the relative link among each of the signals for each condition. Table 2 shows the adopted relative weight of each sensor’s data under the developing condition.

Table 2— Relative Weight of each Signal (Sensor Data) Relevant to the Developing Condition

Signal PIP

Current (Amps)

MT PDP Liquid Rate Condition

Pump Wear 0.20 0.15 0.15 0.30 0.20

Plugged Intake 0.25 0.20 0.15 0.15 0.25

Gas Interference 0.20 0.25 0.10 0.25 0.20

Tubing Leak 0.25 0.10 0.00 0.30 0.35

Viscous Fluids 0.00 0.30 0.25 0.25 0.20

As an example, see the weights for pump wear. If such a condition is developing, we expect the most affected parameter

to be the pump discharge pressure (PDP), so a 30% weight has been assigned to that sensor. On the other hand, the motor temperature is not expected to be affected as much, so a 15% weight has been assigned to that sensor. Condition Match Index. The most likely ESP condition to occur is expressed by the CMI, which shows the probability of the occurrence of a condition according to the arbitrary ranges reported in Table 3.

Table 3—Condition Index Ranges Used for the Fuzzy Logic System

Range of Index Meaning Color

0.0 to 0.3 Unlikely Green

>0.3 to <0.6 Possible Yellow

>0.6 Likely Red

The results are shown in an easy-to-read dashboard (Fig. 4) ranking the top five conditions; the highest one means it is the

condition with the highest likelihood of occurring or will occur in the short term. In the Fig. 4 example, a well exhibits an unexpected event; according to the fuzzy logic, this event is conditioned to gas interference with a condition index of 0.81, followed by pump wear of 0.72, and tubing leak of 0.71.

Fig. 4—An example of the fuzzy logic analysis results as presented to the user. 

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Automated ESP System Optimization The ESP system optimization is achieved using automatic orchestration tools and a commercial nodal analysis application, which work together to perform the following functions daily:

Model the ESP actual operating point (current condition, red point, Fig. 5). Check the ESP operating conditions to indicate whether the pump is running above or below the limit ranges of the

pump (red lines). Perform electric motor and cable capability check (components capability check). This function check determines

whether or not the cable can provide the required current to the motor and if the ESP motor can generate the required power to drive the pump.

Provide the optimum operating points of the current ESP completion (yellow points), from which the user can select the operating point judged to be the most adequate to the specific situation: either the maximum flow rate compatible with available power or the production at maximum pump efficiency. When the user accepts the condition, the optimum point becomes blue.

Fig. 5—ESP system optimization. Plot of liquid rate in RB/D vs. head shows: optimal operating points (yellow), user-selected optimal point (blue), point representing value based on real time condition (green triangle), and point based on last 24 hr condition (red).  ESP Smart Flow Case History: Production Opportunity To minimize current shutdowns, Well-01 was equipped with a variable speed drive (VSD) that allows an automated frequency adjustment, which when enabled, helps to control the free gas fluctuations. The real-time intake pressure displayed on the monitoring screen suggested that the frequency could be adjusted to increase production. The intake pressure was operating above the bubblepoint pressure, which is in line with the operator's reservoir strategy.

First, we evaluated the current well condition in real time; Fig. 6 (left) shows the nodal analysis model (blue line) matching real-time data (yellow points). We navigate into the Advance Diagnostic fuzzy logic module; Fig. 6 (right) shows the results of fuzzy logic (matching signal in blue and unmatched in red). Four out of five signals match with Gas Interference conditions (which one of the most likelihood values). Matching refers to a signal exhibiting behavior that is more likely for that condition to occur. According to ESP senior experts (onsite in KwIDF Collaboration Center), they inferred that by reducing the choke setting, the FBHP can increase and then gas trapped in the pump can flow to surface. Additionally, they suggested increasing the frequency to increase liquid production.

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Fig. 6—Real-time diagnostics: pump gradient plot is shown with real-time sensors (surface and downhole yellow points) and blue line is real-time well/pump model (left). Real-time signal for Qliq, PIP, PDP, amp and MT, showing condition index values (right).

To validate what the experts suggested, the KwIDF team reviewed this opportunity in the ESP Optimization Module. Fig.

7 (left) shows the optimization plot, which the team used to decide to increase the intake pressure by reducing the choke setting and, in parallel, to increase production by increasing the VSD operating frequency from 43 to 45 Hz. Fig. 7 (right) shows the real-time production plot, where the arrow shows the resulting production increase of 152 STB/D. These images show the system graphics used in the optimization process for the ESP smart flow.

Fig. 7—Real-time ESP pump optimization chart showing current and new optimum conditions (left). Real-time production profile after changing the frequency from 43 to 45 Hz. Note that production increases to 1,400 STB/D (right).

How the ESP Smart Flow has Improved Asset Team Performance Traditionally, petroleum engineers monitor, diagnose and optimize a single well on a daily basis by accessing disparate data sources and using commercial petroleum engineering software. This work can be somewhat repetitive in nature and involves some precursor activities, such as collecting data, conditioning data, updating well models, performing sensitivities, and running multiple optimization scenarios. These precursor activities can take from several hours to days for the average experienced engineer and are normally considered to be low-level tasks. These repetitive, low-value tasks often consume up to 80% of an experienced engineer's valuable time—time that could be better spent solving high-value problems.

Using automated, intelligent workflows, many of the low-level precursor activities are automated and can be executed in just a few minutes, allowing engineers to focus on the high-value problems, including time to collaborate with asset team members from other disciplines.

In Fig. 8, the left chart shows a manual process for an experienced production engineer to optimize ESP performance. Typically, the process takes approximately 7 hr per well, including non-value-added activities or non-productivity time (NPT, in blue), which is more than 90%. With the ESP smart flows (Fig. 8, right), the low value-added tasks are minimized

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allowing more time for analysis and collaboration. With this approach, even less-experienced engineers can analyze more wells per day.

Fig. 8—With traditional workflows, a production engineer only has time to analyze and optimize just one ESP well per day (left). With the advanced knowledge and time-savings from smart flows, an engineer can optimize several wells per day (right).

Use of the ESP smart flow includes these benefits:

A system that improves production engineers' efficiency by 10 times, compared with typical mostly manual processes that can take hours or days.

The workflow automation acts as a “facilitator” for junior and non-production engineers. The automated work processes significantly simplify daily well surveillance activities and the conversion of raw

data to information and, in turn, information into action. Using the automated work processes, the production of the Sabriyah field pilot wells has significantly increased. The current ESP running conditions can be easily identified and the required actions for production enhancement

recognized and implemented. The online diagnostic interfaces provide important tools to assist in avoiding or reducing catastrophic events. The effectiveness of the diagnostic tools can be further improved if they are tailored for the specific operational

parameters, taking into account the past events for the tuning of process algorithms. This new way to work implies the adoption of a transparent approach: the data, the analysis results, and the planned

actions are visible to all team members and historically tracked. This is a strong engine to activate strong cooperation and a proactive approach among all team members and disciplines.

Conclusion The ESP smart flow is a very effective ESP diagnostic and optimization tool, reducing information overload and accelerating decision-making using automated processes for data gathering and managing information flows. The smart flow gives engineering teams an advanced tool to access data from the field in real time and, through a set of automated workflow processes, performs diagnostics that facilitate faster analysis and better complex decisions for production optimization. The ESP smart flow is a very efficient combination of conventional analytical methods and a fuzzy-logic rules-based expert system, which is fundamental to move beyond the limits of conventional methods and realize new value from producing assets. Acknowledgements The authors are grateful to Kuwait Oil Company and Halliburton for allowing this paper to be published. Special thanks to our colleagues who worked so hard during the execution of the KwIDF Sabriyah project, especially to Halliburton CPM and Landmark teams in Houston and Kuwait. Special gratitude to H. Al-Zaabi (KOC), Doug W. Johnson, Michael Scott, Carlos Lopez, Jeff Kain, Steven Knabe, Andreas Karakostas, Atif Rabbani, Alex Dunbar, Soyara Cerda, Mohamed Aslam, Kaiwan Bharucha, Christian Stambouli, Joydeep Dhar, Amit Baweja, Houg Ge, Hana Sztarkman, Youssef Al Moussaoui, Shakeel Shaikh, Marcus Simms and Neil Tunney for their extraordinary support during the execution of this project.

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