IPTC 17413 ESP ESP Well Well Surveillance usi ng Pattern Recogn Recogn iti on Analysis , Oil Oil Wells, Petro Petro leum Development Development Oman Oman A. Awaid, H. Al-Muqbali, A. Al-Bimani, Al-Bimani, Z. Al-Yazeedi, H. Al-Sukaity, K. Al-Harthy – Petroleum Development Oman; Alastair Baillie – Engineering Insight Ltd, Aberdeen
Copyright 2014, International Petroleum Technology Conference This paper was prepared for presentation at the International Petroleum Technology Conference held in Doha, Qatar, 20–22 January 2014. This paper was selected for presentation by an IPTC Programme Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the International Petroleum Technology Conference and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the International Petroleum Technology Conference, its officers, or members. Papers presented at IPTC are subject to publication review by Sponsor Society Committees of IPTC. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the International Petroleum Technology Conference 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 where and by whom the paper was presented. Write Librarian, IPTC, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax +1-972-952-9435
Ab st rac t
PDO is managing some 850 Electrical Submersible Pump (ESP) systems scattered across North & South fields, which is continue to grow in the next five years business plan. All ESP wells have real time down-hole sensors that measures intake and discharge pressures, intake and motor temperatures, vibration and current leakage. The oil producing fields are equipped with real time data transmission system where several data measurements; down hole (such as pump intake and discharge pressures and temperatures) and surface (such as volts, amps and frequency) are transmitted directly from the well site to the gathering stations, central control rooms and even to the engineers’ desktop. At present, PDO is deploying an integrated smart tool which will monitor, control, and optimize oil production and ESP performance to the various disciplines involved in oil production and optimization like Reservoir and Petroleum Engineers, Programmers, and Field Operation Teams. However, in order to enable these modern well surveillance systems, which often produce an over whelming quantity of information but the d ata is often misleading or difficult to interpret, establishing the Pattern recognition of the trended real time data is key to make the software intelligent enough to be effective to the work places. This paper will demonstrate how precise ESP, well and reservoir performance can be pre dicted from simple physical relationships and how these relate to the trends of surface and downhole data. A number of real field examples of data trends will be shown to illustrate how a proper understanding of these patterns will allow prompt ESP troubleshooting and ensure the correct actions are taken. The results are correlated with equipment pull and inspection reports to validate the diagnosis. Pattern recognition trends and analysis will be presented for common problems such as hole in tubing, shut in at surface, ESP wear, blockage at pump intake, debris in pump, broken shaft, change in reservoir pressure, blockage at perforations, etc. A proper understanding of these trends will allow the correct settings of alarm and trips and assist in the implementation of semi-automated well surveillance and diagnostic system which being currently deployed in the Company. A pattern recogn ition analysis check sheet will be included in the paper to allow users to quickly interpret data trends and diagnose well, ESP and reservoir performance problems.
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Introduction
PDO is operating some 850 ESPs in various fields across PDO concession area. The ESPs population is continuing to grow in the nex t 5 years as indicated in the company’s Business Plan. Fields / Wells Background The ESP fields are varied in term of reservoir type, type, fluid type, production range, production reservoir mechanisms, well construction & completion schemes, etc. In the North , the ESP fields are mainly carbonates reservoirs and are produced with water flood. The produced oil is mainly light (30 to 40 API); medium to high gross rate (up to 1400 m3/d) at high water cut (up to 96%). The new ne w fields are cutting high net oil (0 to 20% BSW) at medium gross rates (up to 600 m3/d). The wells are completed as vertical, horizontal and even multilateral wells norm ally single zones. In the South , the ESP fields are mainly sandstones and the wells are completed with sand contro l. The production is mainly on depletion drive, with the o ld filed cutting high water cut (up to 96% BSW). The wells are producing medium gross rate (up to 800 m 3/d). The wells are completed as vertical and horizontal wells with some layers commingled. Surveillance using current set-up It is very common in PDO to monitor, anal yze & diagnostic ESP wells using measured real time surface & downhole data. The routine ESP surveillance fosters to work efficiently & enhance understand ing of the ESP performance as well as reservoir behaviors. Real time data and Monitoring Acquisition Down-Hole: All the ESPs wells are equipped with down- hole sensors mainly of enhanced type which measures Intake Pressure (Pi); Discharge Pressure (Pd); Motor Winding Temperature (Tm); Pump Intake Temperature (Tpi)
to measure Tubing Head Pressure. The Casing Acquisition Surface : Each well has a transducer at the Well Head to Head pressure is measured at A annulus by another transducer. Flow system: The production is measured mainly by three or two Phase Separat ors. In some remote fields / new small fields, mobile production meters are used and in some cases a Well Test Unit is used to measure the production (Gross liquid rate; Oil rate; produced Gas rate. Well Head samples are taken routinely to measure BSW and calibrate water cut. SCADA System: All PDO wells are connected at real time to read surface & downhole data of wells, f acilities and stations including production data. Data are then sent to the Remote Terminal Unit (RTU) which in turn sends the data using wireless technology to the Fieldware (now uploaded to LOWIS to some of the wells/fields) server in in the field. Figure 2 below shows the different stages that data goes through from the source to the user.Ref.1
BeamLift Wells RP C
R PC
OPC OPC Layer
ESPs, GL, WI, PCP Wells R TU
OPC Layer
RTU
OPCLayer OPC
RTU
MFM
FieldBus Control System
OPC Layer OPC OPC Layer
FieldWare and RT Applications OPC-PI direct link
Corp DBs & SAP
Production DB
PI
Shurooq .NET Web Server
WEB
Figure 1
Integrated well system surveillance tool in use [ LOWIS / NIBRAS ] SHUROOQ: This in-house web based system which captured and present all the real time data of wells, facilities & stations which can be accessed anywhere across Company server. The data is mainly displayed by trending. All wells have real time trending plots while for ESP wells, a more detail downhole data ar e displayed for a thorough thoroug h ESP analysis & diagnostic. Shurooq will be supers eded by NIBRAS. NIBRAS (will supersede Shurooq):
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Is an in-house web-portal based to ol, a smart platform for Well & Reservoir Management (WRM).The tool functions includes the standard data sources, web-accessibility, multi-levels of data presentation, exception-based surveillance and it is compatible with third-part y tools. Hence NIBRAS, to some degree, can b e able to identify well & reservoir related issues earlier for an optimum decision. The idea of NIBRAS is to integrate the all PDO Assets thus by bringing consistency across the Company in retrieving data from different sources, and in presenting data (pressure and production; surface & downhole) in different formats. The tool does not possess capability of Nodal Analysis, hence it remain as a data trending tool. LOWIS (eventually, once fully capable, will supersede FieldWare): Is a web-based well management software, designed to help improve integrated monitoring and optimization for oil and gas production operation. The tool architecturally aimed to integrate and optimize workflows for optimum monitoring & optimization. Fieldware is being replaced with LOWIS tool. How we identify the ESP problems In PDO, main failure types are categorized into the following groups: i. Equipment related ii. Well condition related iii. Unknown: those which either still under investigation / discussion or are difficult to be categorized due to the lack of the information. PDO failure notification process:
Each major field or a group of fields is equipped with Computer Control rooms (CCR) which display all the wells at real time status around the clock where any abnormality is notified and attended accordingly. Once the control rooms Identify trips, the action on verification & trouble shooting is started. 1. Verify and confirm the trip. The trips setting values are checked & verified if are still applicable at the current time. Trips parameters which are common used in ESPs are: Overload / Under load current; Current Imbalance; Over / Under voltage; Voltage Unbalance & surface Pressures ( THP; F/L P) and Subsurface Intake Pressure & Motor Temperature. Appendix 1 gives the details of Trip settings & values. . 2. Trouble shooting by ESP vendor at well site. The CCR technician / Production Supervisor will notify the ESP Vendor to physically check the ESP W ell when the well stops producing. The tr ouble shooting activity can be as s imple as re-adjust the Alarm settings to back flush through the casing Annulus to Tubing or rock the well, re-configure the Controller, etc. Some tripped ESP wells can be recovered and s ome have to be worked over. There are times, the complex trips will miss the opportunity to be recovered due to the well site competence. In addition, it is vital to have competent personal field support to avoid un-necessary trips which eventually could end up on failures. 3. Confirm ESP failure. ESP vendor has to inform the CCR / Production Supervisor that the ESP is confirmed failed. Average days taken on this step of the Process can be from 1 to one week. Although the real time trend is normally used alongside other surface measured parameters in investigation, but in some cases, the analysis become inconclusive, and the trouble shooting activity takes longer time than expected with additional acticitivities to the wells / surface ESP facility such as back flush, hard start; etc. 4. Types of failures: In most cases, one of the pre-pull activities will be the trouble shooting in attempt to recover the tripped ESP well. In case of un-successful trouble s hooting, the tripped ESP will be conf irmed a failure where will be categorized as either Mechanical or Electrical except if it has clear evidence of Hole in the Tubing. If still there is no clear evidence of pin point the failure code upon pull out of hole, the failure codes remain as Mechanical or Electrical. Although well condition can be easily related to many failures, but the root cause of failure can only be confirmed after a DIFA analysis.
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Armour washed out, high pressure gas ingress in
Figure 2
Impeller full of scale
Figure 3
Why we need Pattern Recogn ition analysis When we look the historical trend of ESPs failures, the majority of the failures are repetitive; it is well known that most of the observed ESPs failures have b een seen in the past. W e are still spending a tremendous time (days to a week) to confirm a failure or in some cases to even differentiate between a failure and a trip. By developing an agreed analytical technical and mathematical tested Pattern t o all the common failures and trips in PDO’s ESP operation, the failures will be identified and even be able to be predicted faster for a quick reaction and optimum solution. In addition, pattern Recognition will enable the engineers to monitor huge data in a particular field for a group of wells.
As it can be seen, the ESP failures are repetitive at increasing trend in some cases. The Pattern Recognition analysis will enable to pin point the abnormal ESP operation case prior to trips or failures. In addition, this level of analysis will assist in clarifying the trips / f ailure reason for a fast reaction thereby minimizing downtime and associated oil deferment.
Figure 4
Figure 5
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Is there any tool to predict the failure? Can Pattern Recognition support the effort?
There are tremendous efforts worldwide from Operating Companies to Service Companies in establishing and developing a failure predicting tool through various approach, such as statitisticaly; technically; analytically, etc. As the failures are repeating, and with the vast real time data, the failure can be predicted through ESP EBS approach once the Pattern Recognition analysis cases have been tested using Nodal Analytical Tools while cross checking with the Real Time trend, the common trips and failures can be predicted for pro active trouble shooting and remedial work. Common ESP cases (can be failure causes)
During ESP operations, there are a number of changes can happen downhole or at surface which can disturb the normal operation of ESP which at the end can cause a trip or failure. These changes can be mechanically; hydraulically or electrically, and can be at the surface; in the tubing / ESP equipment or even in the reservoir. Some of these deviations can actually improve the ESP performance in term of production. After oper ating these fields for many years with the same reservoirs and fluid characteristics; and after experiencing various types of failures while matching with the real time data tre nding, the Pattern was derived from various common cases. Pattern Recognition Cases
PDO have identified 13 cases which can derive a specific unique pattern in term of the change of the measured parameters (variables) which can be used to analyze the ESPs as part of ESP diagnosis. These identified pattern cases are listed as follow: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
Broken shaft Hole in Tubing Blockage at Pump Intake Blockage at Perforations Increase in Water Cut Shut in at Surface Blockage in Pump Stages Increase in Reservoir Pressure Increase of free gas at Pump Intake Wearing Stages (erosion) Increase in Frequency Open Choke (decrease in WHP)
The analysis and diagnosis of four of the above cases will be discussed using its Pattern Recognition from measured variables in Shurooq trending; while matching with Gradient Traverse Plot and Pump Curve from Well Models. In addition, each case, where possible, will be cross checked with the results during Pull out of Holes or Root Cause Analysis (RCA) to support the Pattern Recognition. How to diagnose ESP using Pattern Recogni tion A nalysis How it all started
In 2011 / 2012, PDO conducted a series of ESP on the Job Training at Level 1 and Level 2. The Level 1 was attended by Petroleum Engineers, Operation Engineers , Field Production Engineers, some Well Engineers and Real Time Operation. The course focuses on ESP S ystems Designs, Diagnosis and Optimization.
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Upon completion of Level 1, a small group of participants attended Level 2 where the focusing was more on advanced ESP systems Modeling and Analysis using Nodal Analysis software and in house real time data trending web base system. The Advanced Level 2 courses, done as a workshop, was to build on the basic principles of Prosper modeling and Shurooq surveillance in order to develop, analyse and troubleshoot more complex models or situations and to share this knowledge amongst the various PDO assets. In these Level 2 Workshops, the participants were able to appl y the ESP basic concepts learned from Level 1 to well and ESP models while using the real time data trending. In the Level 2 workshops, after analyzing various real time example cases, the participants together with the ESP Consultant (Course Instructor) and PDO ESP SME were able to build the Pattern for each case by testing each measured variables, which was then developed as a Pattern Recognition Checklist. The Pattern for some cases was tested by the Nodal Analysis software to analyze the Pressure behavior on Pressure Gradient plot & analyzing the Operating Point on Pump curve plot. . ESP Wells Diagnosis - Pattern Recognition An ESP well can be easily and quickly diagnosed by following the logical flow of the equations given. It is important to ensure the below simple logic is followed during the diagnoses stage: -
Above the pump Across the pump Below the pump Reservoir
1) Above the pump (wellhead to pump): Pdischarge = WHP + Pgravity + Pfriction
Fluctuations in Pdischarge are the results of changes in: - WHP - Mixture density in the tubing (i.e. gas or water cut) - Fluid level in the tubing (TVD) - Flow rate, tubing diameter change
∆Pgravity
(~95%)
∆Pfriction
(~5%)
2) Across th e pump : Pintake = Pdischarge where
∆Ppump
and Head depends on:
Ppump
= Head (ft) x Mixture Density (psi/ft) flowrate (inverse relationship) 2 rpm size and type of impeller gas (cavitation) viscosity blockage in stages (sand/debris etc.) reverse rotation
Normal
Adverse
3) Below the pum p (pump to reservoir): Pwf
= Pintake + Pgravity + Pfriction
Variations in Pwf will affect flow from the reservoir. Note that ∆Ppump friction is normally small or zero (casing) 4) Reservoir: Q
= PI (PR - Pwf )
(reservoir inflow performance)
Validates:reservoir inflow performance, flowrate, PI, PR.
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Basic technical conc ept of ESP
As discussed on the equations above, an ESP well can be easily and quickly diagnosed by following the logical flow of the equations, which is now is given on Pressure Gradient plot. While applying the equations, follow the logic above the pump; across the pump; below the pump and at reservoir. The key in the ESP diagnosis is one should not jump to conclusion and start using a gut reaction! For example, if during ESP diagnosis one has a doubt about flowrate, then need to check with the reservoir (check drawdown). Using this process, the behavior of a well can be predicted for any set of conditions.
WHP
Above pump Depth (TVD) :
Across pump
Pi
Pd Below pump Drawdown
Pressure
The variables that can be predicted are as follows - Flowrate - WHP - Amps - Pdischarge - Pintake - Pump dP - Motor temperature
P
PR
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Pattern recognit ion analysis ch eck sheet
Using the physical relationships contained in the equations given above, specific combinations of surface and downhole parameters can be used to describe any change of reservoir, well or ESP performance. These are shown in the check sheet below (Table 1). 1) Broken shaft 2) Hole in tubing 3) Blockage at pump intake 4) Blockage at perforations 5) Increase in watercut 6) Shut in at surface
7) Blockage in pump stages 8) Increase in reservoir pressure 9) Increase of free gas at pump intake 10) Wearing stages (erosion) 11) Increase in frequency 12) Open choke (decrease in WHP)
The arrows indicate the rate of change of the variable. The coloured boxes indicate the unique characteristics of the response. The best trip parameters are indicated by TRIP .
Table 1: Pattern Recognit ion Analysi s checklist
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Field Test to Confirm s imilar r esponse Cases
It is worth mentioning that the following three cases show quite similar responses: • • •
Hole in tubing Blockage at pump intake Blockage in pump stages
These can be differentiated by the following physical tests in the field: 1) Pressure test tubing
Install plug in nipple or prof ile immediately above pump and pressure test with water. This test will immediately identify a hole in the tubing (but not hole in pump section) 2) Shut at surface (short du ration – 5 minutes)
Observe WHP, ∆Ppump and Amps. A hole in tubing should show a small increase in WHP, little or no change in∆Ppump or amps. The volume of fluid circulating around the pump will not change during a surface shut-in, so the operating point on the head (∆Ppump) and load curve (amps) will not change. The blockage at pump intake should give th e highest WHP and ∆Ppump and a decrease in amps. The pump itself (impellers and diffusers) are not damaged in this case and so the pump should still generate the maximum ∆Ppump and WHP. The amps should drop as the operating point moves to zero flow. The blockage in pump stages should show some rise in WHP and ∆Ppump and a decrease in amps. In this case the pump head curve is degraded and therefore there will be not be the full increase in ∆Ppump and WHP as expected. The amps should drop as the operating point moves to zero flow.
Blockage at pump intake Case
WHP
Ppump
Amps
Hole in tubing
Small increase
Little change
Little change
Blockage at pump intake
Big increase
Big increase
Decrease
Blockage in pump stages
Medium increase
Medium increase
Decrease
Head Blockage in pump Hole in tubing
Flowrate
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Field examples usi ng th e Pattern Recognitio n Analy sis ch eck sheet (Table 1)
The field examples of 4 ESP operational cases o ut of the 12 cases captured in the Pattern Recognition Analysis check sheet will be used to discuss the methodology of using Pattern Recognition analysis for the ESP diagnosis, as a key enabler in achieving c onsistency in ESP diagnosis and optimization. The cases will be discussed by first displaying the plots (Pressure Gradient, Pump Curves & Trends) which will signify the predicted theory and will be cross checked with the actua l measured Trend and Nodal Anal ysis models. The diagnosis steps used are as follows: 1.
Cross check with the Pattern Recognition check list using ESP concepts equations (Table 1).
2.
Generate Well Models using Nodal Analysis tool to determine Pressure gradient plots to cross check with the predicted Pressure Gradient plot
3.
Analyze the Pump Curve from Nodal Analysis model to infer the operating point due to the operational case compares to the normal operating point (pre-abnormal operation).
4.
Where available, the pull report and DIFA report will be shown to confirm the Pattern Case. The other cases from the Checklist will be discussed in the paper Presentation.
Broken shaft Pattern Recogni tion Descripti on (Theory)
Broken Shaft matchi ng Pattern Recognition (Field Example)
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Broken shaft on restart
Descrip tion on m easured data trend (real time Web Monitor ing Data System) match Brok en Shaft cases:
- No change in WHP, discharge or intake pressures on restart (Feb 3) - Discharge pressure much lower than normal, indicating fluid level below surface (therefore, no flow) - Zero pump dP (at no flow should be at maximum head, unless zero s tages rotating) (trip parameter ) - Sharp spike in current indicates shaft shear on restart, then much lower than normal (trip parameter ) - “Gas separator shaft found sheared from bottom and sheared part stuck on coupling” (pull report) Be ore break
Operating point (before break) Operating point (after break)
Pull Out Report showing the broken shaft of the gas separator
Hole in the Tubing Pattern Recognitio n Description (Theory)
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TREND PLOT
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Hole in the Tubing - recir culation (Field Example)
Hole located at joint 79 from surface (approx. 270 m above pump)
Descrip tion on m easured data trend (real time Web Monitor ing Data System) match Hole in the Tubing
- Slow decline in WHP (commencing around Feb 25) indiciating a drop in flowrate at surface - Slow, small decline in current (corresponds moving along the load curve to the right, i.e. high flowrates) - Slow decline in discharge pressure ( exactly matched decline in WHP) - Slow increase in intake pressure (indicating a decline in pump dP or head as flowrate increases) - Delayed but then sharp rise in measured temperatures (lag due to sensor location at motor base) - Final, stabilised pump dP is determined by hole location and size (best trip parameter ) Pump Curve: To continue with the diagnosis, the nodal analysis (on quick look functionality) is used to generate the operating point on Pump curve as shown below, which indicate that the head has dropped while the Pump flow rate (Q) is on the up thrust position indicating a recirculation case. The slight observed decline in current is mainly due to the corresponding load curve which is usually lower at very high flowrates. . Before circulation
Operating point (normal)
Block age at Perforations Pattern Recognition Descrip tion (Theory) Operating point (recirculation)
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TREND PLOT
Amp
Time
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Blockage at Perforatio n Real Example Blockage at Perforations
Descrip tion on m easured data trend (real time Web Monitor ing Data System) match Bloc kage at Perforations
- Multiple pump starts with regular trips on low intake pressure (“pump off”) - Slow decline in WHP over 1 da y flow period (variations indicates flowline slugging = low flow) - Slow decline in discharge pressure ( mirrors decline in WHP, also slugging in wellbore = low flow) - Variation in current matches slugging cycles (load changes with flowrate) - Intake pressure continuously declining over flow period - Decreasing flowrate (from WHP) and decreasing intake pressure indicate a reservoir problem (IPR)
Inflow performance curve
Operating point (close to pump off)
High pump dP with low intake pressure
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Increase in water cut Pattern Recogniti on Descripti on (Theory)
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Increase in Water Cut Real Example
Initial flow up casing
Descrip tion o n measured data trend (real time Web Monito ring Data System) match Increase in WC
- Very gradual decline in WHP indiciating a drop in friction in flowrate at surface (less gas, more water) - Slow increase in current (load increasing due to pump fluid density increasing) - Slow increase in discharge pressure (heavier fluid in tubing) - Slow increase in pump dP (= head * fluid density) as water cut increases - No change in motor temperature
Low WC
Operating Working toward ESP Exception Base Surveillance (ESP EBS) point (52% Operating point (89%
High WC
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Since all the ESP wells have down-hole sensors and are on real time monitoring, the Pattern Recognition Analysis can be grouped monitored as Exception Base Surveillance by introducing Alarms and Trips values. Setting Alarms and Trips
In order to achieve the ESP EBS, it is essential to determine the right values for Alarms & Trips. In some cases, the values may vary per well as well per field. Using Pattern Recognition check list, it is aimed to apply these trends into Alarms & Trips setting. In this section, the setting of Alarms & Trips in relation to Pattern Recognition check list will be discussed. The ongoing attempt on using this Check list to the in-house well web-base s ystem, NIBRAS, will be discussed and the plan to introduce this unique ESP EBS in LOWIS will be discussed. In t he mean time, the Pattern Recognition Checklist is used on individual case basis while in the future is to integrate in the LOWIS software as additional functionality on ESP EBS. There are three key major parameters for setting Alarms and Trips, namely: 1. Amps:
Overload – usually set at +15% of running Amps Under load – usually set at 1 15% or 20% of running amps, but could be 40% f or gassy wells or pumps with a load curve that decreases sharply at low flow rates. 2. Surface and Downhole Pressure
These provide the best protection since they respond immediately to a problem. Wellhead pressure (high trip) indicates a shut in at surface downstream of the WHP sensor (e.g. choke). Pump discharge pressure (high trip) indicates a shut in at surface or restriction in the tubing or tree. Pump discharge pressure (low trip) indicates loss of flow and fluid level below surface. Pump intake pressure (low alarm or trip) indicates loss of inflow (pump off). ∆Ppump (high
alarm or trip) indicates operation in down thrust (zero or low flow). ∆Ppump (low alarm or trip) indicates operation in up thrust (high flow, usually recirculation).
All of these parameters can be calculated using the equations given on page 2 or from software such as Prosper or SubPump or Autograph. ∆Ppump gives the best indication of pump operating condition. Note that the ∆Ppump settings are dependent on density and frequency so must be recalculated accordingly. 3. Motor Temperature (high trip)
This is a lagging indicator, since the greatest source of heat is usually the pump which is a long way from the sensor (located at the motor base). It is usually set at 1 25 ºC, but better to be customized to the well conditions by setting at normal running temperature plus, for example, 15 ºC.
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Testing ESP EBS using Pattern Recognition Analysis in NIBRAS
Two cases were tested to establish ESP EBS in NIBRAS Alarm Defi ni tion Alarm Name Hole in Tubing Shut in at Surface
Current
Motor Temp
THP
Same or slight decline ↔ OR ↓ Decline ↓-15% of set
Slowly increase ↗
Slowly increase ↗
Intake Pressure
Slowly increase ↘ -5% of set
Discharge Pressure Decline by 5% ↘ -5% of set
Increase by 10% ↑↑+10% of set
Increase by 10% ↑↑+10% of set
Increase ↑
Slowly increase ↗
Table 2 Alarm setting in Nibras ESP EBS NIBRAS test server was created and Alarms value / range were put forward followed by Pattern Recognition checklist for ESP EBS. It is expected that som e of the values might need revision as the rate of change of some variables, such as Temp / Amps might vary fr om well to well and from field to f ield. It has been realized that there are some Pattern Recognition cases with similar Alarm values such as Hole in the Tubing and the Blockage at Pump Intake would be difficult to achieve ESP EBS by o nly using Pattern Recognition trending. The ESP EBS needs to include the W ell Model to show the Pump Curve where the Amps are slightly decreased due to the power load current at high flow rate. Hence, this demonstrates the limitation of Trending tools without proper nodal analysis software. Way forw ard Testing ESP EBS using Pattern Recognition Analysis in - LOWIS.
It is important that well surveillance integrated systems have the capability of well modeling to pro per use the Pattern Recognition Check list in ESP diagnosis in t he report such as ESP EBS. Hence tool such as LOWIS is key in using Pattern Recognition in ESP diagnosis because of its ability of performing W ell Models (Nodal Analysis), which a basic limitation element for the in-house web base report portal systems - NIBRAS. Currently, PDO is integrating pattern recognition analysis into Nodal Analysis tool. All identified cases will be probably implemented to Nodal analysis to reflect the benefit of this level of advance diagnoses. Eventually, Pattern Recognition will be fully automated in real time, web base Nodal Analysis too l to enable the analysis & diagnoses of huge data in a particular field. Challenges:
To be able to achieve full integration of pattern recognition into real time, web base Nodal Analysis software. Keeping all the models are up to date with most recent well test & subsurface data. It is important that all transmitted data are accurate and maintain. In addition, Human inter-action is critical to ensure corrective actions are taking on bored.
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Conclusions:
A Pattern Recognition analysis check sheet is being used in ESP diagnosis which enables a quick and consistency interpretation across PDO in those identified 12 cases. Few cases were demonstrated in PDO by matching the Pattern Recognition analysis tool with pulled out ESP reports. Few of the Pattern Recognition cases which have similar response trends needs a further field test to confirm the case; for example between Hole in Tubing; Block age at pump intake and Blockage in pump stages. ESP well and reservoir performance can be precisely predicted from simple physical relationships b y matching with the real time trends of surface and downhole data. Establishing the Pattern recognition of the trended real time data is a key to make the software intelligent enough to be effective to the work places. As a way forward in ESP monitoring, the work is ongoing in establishing the ESP EBS using Pattern Recognition in the integrated web-base Nodal Anal ysis software (LOWIS) by combining real time with the well models by building corresponding logics. Applying Pattern recognition principle can significantly increase in ESP up time / run life will be achieved. Ap pen di x 1: Tri p Par amet ers & Valu es
Current a. Current Overload Trip - Overload setting is used to trip the ESP motor when motor current exceeds this value thereby preventing the motor from getting damaged. We set overload at 115 % of motor nameplate amps. For example if motor nameplate amps is 40 A, overload setting will be 40 * 1.15 = 4 6 A. b. Current Underload Trip -- Underload setting is used to trip the ESP motor when motor current drops be low this value thereby preventing the motor from getting damaged. We set underload at 80 % of motor running amps. For example if m otor nameplate amps is 40 A, and motor running amps is 35 A, then underload setting will be 35 * 0.8 = 28 A. c. Current Unbalance Trip -- Current unbalance trip setting is used to trip the ESP motor when unbalance in the three phase currents of motor exceeds this value. We set current unbalance trip setting at 20 % or 25 %. So if the unbalance between the three phase currents of the motor exceeds 20 % or 25 %, it will trip the ESP motor. Voltage d. Overvoltage Trip -- Overvoltage trip setting is used to trip the ESP motor when incoming voltage to the m otor exceeds this value. We set overvoltage trip at 110 % of required motor voltage. For example if required motor voltage is 1700 V, then overvoltage trip setting will be 1700 * 1.1 = 1870 V. e. Undervoltage Trip -- Undervoltage trip setting is used to trip the ESP motor when incoming voltage to the motor drops below this value. We set undervoltage trip at 90 % of required motor voltage. For example if required motor voltage is 1700 V, then undervoltage trip setting will be 1700 * 0.9 = 1530 V. f. Voltage Unbalance Trip -- Voltage unbalance trip s etting is used to trip the ESP motor when unbalance in the three phase voltages of motor exceeds this value. We set voltage unbalance trip setting at 6 % or 8 %. So if the unbalance between the three phase voltages of the motor exceeds 6 % or 8 %, it will trip the ESP motor. Surface Pressure g. High Tubing Head Pressure -- This s etting is done in the pressure switch which is installed in the tubing line of wellhead. The pressure switch will trip the ESP motor when tubing pressure exceeds the setting value. Generally high tubing pressure trip setting is 35 or 50 bar (In Yibal / Alhuwaisa field).
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h. High Flowline Pressure -- This setting is done in the pressure switch which is installed in the flowline. The pressure switch will trip the ESP motor when f lowline pressure exceeds the setting value. Generally high flowline pressure trip setting is 14 or 26 bar (In Yibal / Alhuwaisa field) depending on GRE or carbon steel flowline. Subsurface Pressure/Temperature a. Low Pump Intake Pressure -- Low PIP trip setting is used to trip the ESP motor when pump intake pressure drops below the setting value. This setting is different for different wells depending on the PIP of each particular well. b. Pump Motor Temperature Trip -- Motor temperature trip setting is used to trip the ESP motor when motor
temperature rises above this setting value. Generally we set the motor temperature trip at 125 or 130 degree Celsius. Nomenclature ESP Electrical Submersible Pump Pi Pump Intake Pressure Pd Pump Discharge Pressure Tm Motor Temperature Tpi Pump Intake Temperature API American Petroleum Industry BSW Base Sediments & Water THP Tubing Head Pressure TVD True Vertical Depth
EBS AL PDO MOG ESP-SME VU CU SCADA
Exception base surveillance Artificial Lift Petroleum Development Oman Ministry of Oil & Gas ESP-Subject Matter Expert Voltage Under load Current Under load Surveillance Computer Assisted Data Acquisition
Figures and tables
Figure 1
Real time flow chart
Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7
Cable failure Impeller Scale deposition Repetitive tubing leak plot Repetitive mechanical failures Pull out broken shaft picture Pull out hole in tubing picture
Table 1 Table 2
Patterns Recognition Analysis checklist Alarm setting in Nibras ESP EBS
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IPTC 17413
References
1.
Electrical Submersible Pumping System: Striving for Sustainable Run-Life Improvement in Oman oil; by Atika Al-Bimani & Samuel Armacanqui; Buthaina Al-Barwani; Iqbal Sipra; Said Al-Hajri & Halima Al-Riyami; IPTC – 12601; 2008 Kuala Lumpur – Malaysia
2.
Focus ESP Surveillance in Sensitive Conditions: Benefits & Challenges by Ibrahim Al-Siyabi, Hamed Alth Sharji, Atika Al-Bimani; - PDO: 24 Annual ESP Workshop in Gulf Coast – Houston April-2007
3.
PDO Level 1 Training Course; ESP Systems Design, Diagnosis and Optimization © Engineering Insights Limited, 2011/2012
4.
PDO Level 2 Training Course; Advanced ESP Systems Modeling & Analysis Workshop using Prosper & Shurooq © Engineering Insights Limited, 2011/2012
Ac kn ow led gem ent s
The authors acknowledge the support of the Ministry of Oil and Gas, Oman (MOG), and the management of Petroleum Development Oman for providing the environment to do this work and granting permission for this paper to be published. In addition, the authors acknowledge the North & South Directorates, Petroleum Engineering Directorates & supporting directorates (Well Engineering, & Automation) for their endless contribution in making the company’s ESP management and operation a success.
