NEW SENSOR DEVELOPMENT HELPS OPTIMIZE PRODUCTION

PETROPHYSICS, VOL. 53, NO.2 (APRIL 2012); PAGE 70-85; 9 FIGURES;

NEW SENSOR DEVELOPMENT HELPS OPTIMIZE PRODUCTIONLOGGING DATAACQUISITION IN HORIZONTAL WELLSl

Adrian Zett2, Michael Webster2,Alwin Noordermeer2, Mitchell Hockley3,Glynn Lockyer3, Hugh Browne3,Charles Donkin3

ABSTRACT

As horizontal wells become increasingly common, theneed to make measurements to optimize well health andmanage the reservoir also increases. Production logging inhorizontal wells with multiphase flow presents data acqui-sition challenges in the form of conveyance method andsensor selection.

In this paper we present an approach to logging a hori-zontal well using new generation array sensors. The well wascased and perforated with high water cut and sand produc-tion. Logging while tractoring a combination of conventional(centralized) and array sensors provided a good quality dataset that enabled successful identification and shut-off of sand

and water. The use of a new impeller design in a mini-spin-ner array helped overcome the challenges sand productionposed in the well. Smaller mini-spinner thresholds improvedthe velocity profile, allowing us to identify water recircula-tion at very low rates. An array resistance measurement pro-vided an estimate of water holdup.

Tool rotation proved an important benefit for array sen-sors run in this harsh environment. Tool rotation helped keepthe sensors free from debris while the traditional centralisedspinner showed an increased tendency for jamming whenintersecting sand accumulations in casing troughs or restric-tions along the well trajectory. In addition, the rotating arraysensors provide good circumferential coverage in the well.

A sand detection instrument was used in combinationwith the production logging string. The instrument is com-binable with the production logging string and simultane-ous data acquisition permits to integrate all sensors, allow-ing a robust interpretation of sand influx and water salinity.

This paper focuses on the acquisition of new genera-tion array data to help meet the well intervention objectivesleading to a commercial and technically successful out-come. It identifies the strengths and gaps of current tech-nology relative to existing challenges in horizontal wellsand the need to move to array measurements.

INTRODUCTION

The candidate well is located in th~ Mungo field (Cen-tral North Sea, Zett et aI., 2008) and was completed as agas-lift producer in September 2009. At the time of logging(August 2010), a well test produced 671 bbl/d oil, 1173 bbl/dwater and 0.27 MMscf/d gas.

The well conditions were unknown before the well-work, as there had been no intervention since the well wasput on line. The well has had to be choked back to approxi-mately 10% of capacity due to high sand production sincethe very start of production.

The well was drilled overbalanced, with no evidence ofseepage losses or open fractures. The well has shown signifi-

cant depletion, indicating connectivity between the Palaeo-cene reservoir and the Ekofisk injected sands.

The production log was part of a complex programdesigned to understand the well performance and moni-tor gas, oil and water production. The well was accessedfrom the Mungo platform, an un-manned installation with arestricted number of personnel onboard.

ARRAY SENSORS

History and Evolution of MAPS Tools

The development of the MAPS (Multiple Array Produc-tion Suite) started with the first CAT (Capacitance Array

Manuscript received by the Editor February 2,2012; revised manuscript received March 13,2012.10riginally presented at the SPWLA 52nd Annual Logging Symposium, Colorado Springs, CO, USA, May 14-18,2011.2BP, 1 Wellheads Avenue, Dyce, Aberdeen AB2l 7PB, UK; Email: [email protected] Oilfield Technology, 58 Howe Moss Drive Kirkhill Industrial Estate, Dyce, Aberdeen AB2l OGL, UK

rg2012 Society of Petro physicists and Well Log Analysts. All Rights Reserved.

70 PETROPHYSICS April 2012

Supplied by the NIOC Central Library

New Sensor Development Helps Optimize Production Logging Data Acquisition in Horizontal Wells

Tool) in 1999. This tool had twelve sensors mounted onstraight arms that were motored to open & close such thatarms were alternately on a larger or smaller radius, thusgiving a cross-sectional view of the phase distribution. Adisadvantage of this tool was that it could only be loggedon up-passes or stationary readings. It is generally acceptedthat down passes against the flow provide the best data; thusthe CAT developed to its present configuration of twelvesensors mounted on bowsprings that open out to 7 in.

With the increasing trend to horizontal wells and multi-lat-erals, there was a clear need for production logging sensors suit-able for these high-angle conditions. The SAT (Spinner ArrayTool) and RAT (Resistance Array Tool) were then developedon the tried-and-tested mechanical configuration of the CAT.

The spinners on the SAT are offset to the side of the bow-spring to allow an unimpeded view of the flow, hence only sixsmall diameter spinners could be mounted to meet the con-straint that the tool should have a closed diameter of 1-11/16in.The first tools, launched in 2008, used the traditionalHall Effectswitch as the sensor, but this required a minimal clearancebetween the spinner and the sensor, often resulting in jammedspinners due to ferrous debris. A new sensor was developed toallow a larger clearance eliminating the jamming problem, anda variant of the SAT was designed to accommodate spinnerswith a 50% larger diameter - this significantly reduces thresh-old velocities but also limits the tool closed diameter to 2-1/8 in.

The RAT was launched simultaneously with the SAT, butunlike the CAT, the RAT has a very small probe so that it candetect small hydrocarbon bubbles.

Whilst the emphasis has been on horizontal wells, both theSAT & RAT will be equally effective in lower deviation wellswith a high water hold-up, a common problem in more maturefields.

Spinner Array Tool (SAT)

The Spinner Array Tool features six miniature spinnersdeployed on bowspring arms, enablingdiscreet local fluidveloc-ities to be measured at 60 degree intervalsaround the wellbore.

Phase segregation occurs in many wells, including eventhose with little deviation from vertical; the lighter phasesmigrate to the high side of the well, the heavier phases to the lowside. The individual phases flow at different velocities and pos-sibly in differentdirections. Historically,correlationshave beenused to estimate phase velocities from the total fluid velocitylog. The SpinnerArray Tool can provide directmeasurement ofindividual phase velocities. Combined with hold up data fromthe Resistance Array Tool (RAT) and Capacitance Array Tool(CAT), it is possible to provide quantitative estimates of thevolumetric flow rate for each phase with a much higher degreeof certainty.

The six miniature spinners are mounted on bowspringarms and use low friction jeweled bearings to reduce themechanical threshold of the spinner, and improve sensitivity

April 2012

to fluid flow. The tool outputs the direction and speed ofspinner rotation. A relative bearing measurement is incorpo-rated to indicate the high side of the hole.

To meet the standard constraint that the tool shouldclose to a minimum OD of 1-11/16 in., the spinner diameterswere limited to 004in.

The output torque of a spinner is proportional to thefourth power of its diameter, ignoring viscous effects (Eq 1).

Torque = 7r*R 4 * (wa - ws) * U * P

If ros, the actual speed of the spinner is zero, the torqueequals the friction torque at the threshold. (roa is the notionalperfect speedofthe spinner in the flow, of speed U & density p)

For example, a OAindiameter spinner of the same pitch asa 0.6 in. diameter spinner only gives 20% of the output torque.If the bearing arrangement is the same for both, then the thresh-old speed of the smaller spinner, at which the spinner starts toturn, will be about five times higher. To accommodate spin-ners with 0.6 in. diameter the tool's minimum closed diameteris 2-1/8 in.

The small spinner must retain sufficient robustness to with-stand deployment downhole. This works against the need tokeep friction minimized. Specialist bearing materials mustbe used. The bearings must support the spinner not only inslow speed wells, but also work in high speed gas wells. Thisrequires bearings capable of operation at speeds larger than10,000 rev/min. The spinners must be stowed into the tool andare protected to some extent from engagement with the casingwalls and other obstructions.

The rotational speed of the spinners must be measuredaccurately through the walls of miniature pressure housings.Traditionally, Hall Effect switches have been used as sensors,but these require relatively strong magnets in close proximityto the switches. The magnets of course attract ferrous debris.Additionally, three magnets would be required to define thedirection of spin.

In the SAT tools, modified GMR (Giant Magneto-resis-tance) sensors have been used (Figure I). The principal advan-tage is that a much weaker magnet is required; furthermore, thistype of sensor can still determine spin direction when only onemagnet is used. The requirement for a weaker magnet allows alarger gap between the sensor and the spinner, thus preventingjamming as any accumulation will be dislodged.

Temperature probes have been built into each of the SATsensor housings.

Resistance Array Tool (RAT)

In horizontal and deviated wells, the lighter mediumtends to float to the high side of the pipe. Lighter fluids alsotend to flow faster in an upward direction, even to the extentthat heavy fluids fall back in the opposite direction to the gen-

PETROPHYSICS 71


Adrian Zett, Michael Webster, Alwin Noordenneer, Mitchell Hockley, Glynn Lockyer, Hugh Browne, Charles Donkin

Sensor PCB wfth GMR sensor

array in pressure housing

Springbow with spinner offset-mounted

Fig. 1 Latest generation SAT sensor.

eral flow. Thisis illustratedin Figure2.The RAT is designed to estimate the relative propor-

tions of water and hydrocarbon. The well water generallycontains sufficient salt to make it significantly lower inresistivity than hydrocarbons, which have very low con-ductivity. By measuring the resistance at different pointsacross the well cross-section, the water to hydrocarbonratio can be measured, and an estimate of water hold-upcalculated.

The RAT incorporates 12 sensors, equally spacedaround the periphery of the tool. By placing the sensorsaround the full circumference of the pipe's cross-section,all variation in fluid type can be monitored. The sensors aredeployed towards the pipe wall using bowsprings, allow-ing the tool to be logged up and down the well. The RATalso includes a deviation and rotation sensor to measure thetool's orientation.

The key sensor mechanics comprise; see figure 3:Probe tip that connects to the sensor electronics input

and reference contact, typically at earth potential. Theelectrodes in the sensor are positioned within a protectiveshroud.

eubbie:s00walerlnoil

Bubbl9li; Of

011 In water

Fig. 2 Water and hydrocarbon separation in deviatedwell.

Soo',';"",.~ , ~ S"~,, o""cod,. n_'

Sensor ,---""-------

electronics : 1Input . "'-Referenceelectrode

Fig. 3 Array Resistance - Sensor overview.

freq

0 Normalised resistance

Fig. 4 Distribution of measured values.

Water and Hydrocarbons Hydrocarbons

Fig. 5 RAT histogram output - Warrior screenshots.

Resistance MeasurementResistance measurements can be made between the

probe tips, or electrodes, at a rate of up to 10,000 samplesper second. Each measurement results in a number that isproportional to the logarithm of the resistance detectedbetween the sensor electrodes, and therefore the fluidresistivity, with a range of 5 decades of resistance.

Each sensor is sampled every 2.4 ms. The resultingdata are formatted in two distinct ways.

The first provides a mean and standard deviation ofthe measured resistance. These values are output for eachsensor, typically six times per second. This gives the RATa measurement resolution of 1 in. when logging at 30 ft/mm.

The second data format is a histogram of resistancemeasurements for each of the 12 sensors. Data for the his-tograms are collected over periods 12 times greater thanthe standard mean and standard deviation measurements.This provides a more detailed view of the measurementdistribution, and can be used to assess the data quality.Each histogram is divided into 16 bins of values. The




distribution of values is illustrated in Figure 4, and fieldexamples of histogram data can be viewed in Figure 5.

Mean and Standard Deviation

For each of the sensors, resistance values are measuredmany times over the period being summarized. The meanresistance value can be calculated as follows:

- "RR=~

n

where

RR

Mean resistanceMeasure resistance values

Number of samples takenn

To calculate the standard deviation, the following func-tion can be used:

0- = ~2J Rn-Ii)'

where(J'

R

R

Standard deviationMean resistanceMeasured resistance values

Number of samples takenn

Hold-Up Calculation

The proportion of water in a well cross-section is com-monly known as hold-up. To determine the hold-up, the fol-lowing calculation should be made:

y _A.vw-A;

whereYwA.v

Water hold upCross sectional area of the well occupiedby water at anyone position along the axisof the well

Cross sectional area of the well, typicallyfrom pipe internal diameter

4

In well conditions where the water and hydrocarbon pointsare well defined, the water hold-up can be estimated for each sen-sor. A simple linear interpolation between the water and hydro-carbon points is used to calculate the hold-up; see Equation 5.

(RhC- R)Y =w (Rhc- Rw)

where

April 2012

~wR

Rw

Rhc

Water hold upMeasured mean resistanceResistance measured in water

Resistance measured in hydrocarbon

In well conditions where only 1 end-point is known,either that of water or hydrocarbon, it is still possible toestimate water hold-up values.

The sensor readings are polarized between a resistanceRhcfor the insulating hydrocarbons and Rw for the conduc-tive saline water. By definition, the mean resistance read-ing will be:

R =(Y w x Rw) + (1 - Y w) X Rhc

whereR Measured mean resistance

Water hold upMeasured mean resistance

Resistance measured in hydrocarbon

~wR

Rhe

Similarly, the standard deviation will be

0' = ~(Yw XO'}) +«(1- Y.Jx O'hc2)+ Y wx (1- YJx (Rhc- Rj

where(J'

Yw

(J'w

(J' he

Rw

Standard deviation of mean resistanceWater hold upStandard deviation of water resistanceStandard deviation of hydrocarbon resistanceResistance measured in water

Resistance measured in hydrocarbonresistance

Rhe

Manipulation of the above equations leads to two for-mulas for calculating water hold-up that are based on themeasured mean resistance, standard deviation and either the

measured hydrocarbon resistance Rhc or water resistance Rw:

(Rhc - RrY =w

(_)

2

Rhe - R + (J'2

2

Y = (J'

w (R- Rwr + (J'2

where

Y w

(J'

RRwRhc

Water hold upStandard deviation of mean resistanceMeasured mean resistanceResistance measured in water

Resistance measured in hydrocarbonEquation 8 can be used to estimate hold-up when only

PETROPHYSICS 73


Adrian Zett, Michael Webster, Alwin Noordermeer, Mitchell Hockley, Glynn Lockyer, Hugh Browne, Charles Donkin

the hydrocarbon point is known, while Equation 9 can beused when only the water point can be measured.

Both formulas only hold for conditions where the distri-bution is polarized between two tight distributions for waterand hydrocarbon.

Salinity Indication

The RAT was designed to determine the mixture ofwater and hydrocarbons. It was not intended to providea definitive indication of the resistivity of the water andthereby its salinity. As the sensors are susceptible to changesin sensitivity through build-up of deposits on the electrodes,component wear and replacement or maintenance, they arenot calibrated for direct reading of fluid conductivity. How-ever, it is recognized that the sensors are sensitive enough togive some insight into the salinity and are capable of high-lighting zonal changes in water salinity in the well.

The greater the salinity, the more conductive the waterbecomes, and the resistance measured by the RAT sensorswill decrease. The standard deviation should also be low toestimate accurately true changes in the water salinity.

The resistance of saline water is affected by both thesalinity and its temperature. Lower resistance values willbe caused either by an increase in the concentration or tem-perature, or a combination of both. It should be noted that achange in temperature from 30 degC to 120 degC will havea similar effect on the measured resistance as an increase in

salinity by a factor of 3.

Capacitance Array Tool (CAT)

The Capacitance Array Tool consists of a set of 12bowsprings that op open outwards from the tool bodyto the casing. A sensor is attached to the inside of eachsensor, approximately 0.5 in. from the bowsprings them-selves.

The principle of operation is similar to conventionalcapacitance water-holdup tools. However, a novel mea-surement circuit has been developed, using the sameprinciples to take capacitance measurements from 12

.~Cx w~ ov

RX

Fig. 6 Sensor Equivalent Circuitith respect to OIL -WATER(i.e. 0.2 - 1.0).

localized sensors. An oil/water calibration curve can thenbe fitted to the measurements to determine the fluid sur-rounding each sensor. Qualitatively, gas produces thehighest frequencies, oil slightly lower frequencies, andwater produces frequencies approximately one third ofthe air frequency.

Within each of the fingers, near the tip, is a miniaturecapacitance sensor. Each sensor, with its associated mea-surement circuitry, produces a frequency output that isrelated to the permittivity of the surrounding fluid. Hencethe fluid (oil, water or gas) surrounding each of the sen-sors can be determined. The total oil/water/gas compo-nent fractions over the cross section of the well can thenbe calculated.

The tool operates on UltrawireTM telemetry and cancollect data in excess of 100 frames per second. However,other parts of the system, e.g. wire line telemetry speed,will usually limit the maximum frame rate significantly.For example, the default configuration of the Warrior log-ging software is set at 24 samples per foot.

There are three types of sensor on the CapacitanceArray Tool. The primary measurement is obtained froman array of capacitance sensors. There is also a relativebearing device and an uncalibrated temperature sensor.

Figure 6 shows the principle of the sensor operation.Essentially, the sensor consists of a logic inverter gate,

configured as an oscillator circuit. There is a fixed feedbackresistor RX from the output to the input. The sensor is con-nected to the input of the gate and the capacitance of this sen-sor, ex is charged and discharged through resistor RX.

The capacitance ex of the sensor is dependent upon thepermittivity ofthe fluid surrounding the sensor. This principleis repeated for each of the 12 sensors.

The gas, water and oil permittivities have the followingvalues:

Gas: Low permittivity (lOr= 1).

Water: Highest permittivity (n = 80, a complex function canbe used for accurate value).

Oil: Intermediate permittivity dependent on the preCIsenature of the oil, n = 5.

Thus, the fluid surrounding each sensor can be iden-tified from the oscillation frequency. It is worth notingthat due to stray capacitance, amongst other things, theoscillation frequency is not 80 times less for water thanit is for air. In practice, the water frequency tends to bearound 20% of the air frequency and the oil frequencytends to be approximately 80% of the air frequency.

As a first step in interpretation, the readings fromeach sensor are normalized, such that oil has a fixed nor-malized value (0.2).



11


This produces normalized readings in the range 0-1,where gas = 0, oil = 0.2 and water = 1.

If Raw Reading;::::Oil Calibration Reading then normal-ize with respect to GAS - OIL (i.e. 0-0.2).

Normalized Reading = 0.2 x (Gas-Raw)/(Gas-Oil)

If Raw Reading < Oil Calibration Reading, normalizewith respect to OIL .. WATER (i.e. 0.2 - 1.0).

Normalized Reading = 0.2 + 0.8 x (Oil-Raw)/(Oil-Water)

There are two possibilities for converting a normalizedreading to an oil/water/gas fraction:

The first method is two or more thresholds can beapplied to the normalized readings as follows (four thresh-olds shown):

This approach is used in the CAT tool module of theWarrior logging software for converting a normalized value

: Calibration Media

--_uu-GA' 51 001L+GA' 5) THR-ESHOLOm

- ,'-c, ,.-n_nnn c~ on

- _00- -m- 000000000000... _hn U

interpn'ta' GAS

to one of five screen colors.The second method is to use an oil-water calibra-

tion curve to determine the water fraction from raw read-ings between the water and oil readings. For raw readingsbetween the oil and gas readings, a linear fit between the oiland gas readings can be assumed.

This is more general than method one. Note that the fullaccuracy of a calibration curve can only be realized if theoil-water mixture forms a fine oil-continuous emulsion.

The second method is the preferred one, as it automati-cally includes the "simple" threshold approach.

If it is known that the tool is in two-component oil-waterflows (i.e. below the bubble point so no gas is present), it

Fig. 7 The tractor used for conveyance.

April 2012

may be advantageous to add any calculated Gas fraction tothe Oil fraction.

Sensors Rotation

Tool rotation represents an important benefit for arraysensors, especially when running in hostile environment. Inaddition, the rotating array sensors provide circumferentialcoverage in the well (more points of measurement).

In case of individual sensors failure, the rotation stillprovide enough data points for an accurate result. It wasobserved (Gysen et aI, 2010) that tool rotating (1 rotation in20 to 40 ft works well) increases the accuracy of the profilesand compensates for probe malfunctioning.

Array Visualization

MAPview is a system for visualization of array tooldata and quicklook volumetric flowrate calculations for eachphase.

Hold-up and tool rotation data from either the CAT(Capacitance Array Tool) or the RAT (Resistance ArrayTool), and fluid velocity and tool rotation data from the SAT(Spinner Array Tool) are imported from the Warrior data-base, as well as deviation and pipe ID data. There is alsoan option to import Central Spinner Tool (CST) for displaypurposes only.

MAPview presents the CAT or RAT data as the colorcoded surface of a cylinder and the SAT data as arrows rep-resenting the location, direction and magnitude of the mea-sured flow of fluid (Appendix 1). A cross-sectional flow pro-file may also be displayed.

For quicklook volumetric flowrate calculations, averagehold-up values are set as threshold values for each phase andwith the pipe ID, the cross-sectional area occupied by eachphase is determined. The local hold-up value at each SATsensor can be determined, thus defining in which phase itlies. The average of the flow velocities for each SAT sen-sor in each phase is used to represent phase velocity. Hence,quicklook volumetric flowrates are calculated and, togetherwith phase velocity data, are written back to the database forlog presentation.

The immediate application for MAPview is for dataquality control and quicklook on location.

CONVEYANCE

Conveyance was achieved through a wireline well trac-tor. The wireline tractor can transport logging instrumentsinto difficult trajectory wells, be they near vertical, horizon-tal or deviated, in order to analyze the well conditions and insome cases evaluate which intervention is required.

The wireline tractor (Figure 7) functions with an elec-tric/hydraulic power relationship.

PETROPHYSICS 75



Fig. 8 Sand production monitoring at surface.

The control panels, communications and internal powersource are electric, while the remaining parts and functionsare hydraulic. When activated, the wheels are hydrauli-cally deployed out of the tool body and automatically startto rotate. Each wheel contains its own independent hydrau-lic motor, which facilitates the forward motion in the well.The tool centralizes itself in the well bore once contact withcasing has been established and then deploys interventiontools into the well bore, taking the string beyond its originalhang-up point. The passive and immediate fail-safe featureensures that the wheels are retracted automatically when itis powered down or disconnected. This type of tractor wasconsidered for the following reasons:

. Variable logging speed, being configured for higherspeeds and can also cope with low 5 mImin speed. Smoother data acquisition (less well fluid disturbance)removing interpretation uncertainty. Debris tolerance, by design the wheeled tractor provides apassage for any debris present to 'fall out'

. Compatible with any / all logging toolstring when used inconjunction with Tri-Core/Multicon Cable (logging whiletractoring)

The total distance tractored in the Mungo well summed8418m. The quality of data recorded while tractoring (againstthe flow) can be seen in Appendix 5 and 7.

TOOLSTRING

For a complete cross-sectional flow characterisation,

.m

'.".,

t::

Fig. 9 Well trajectory and location of perforations.

centralised sensors have to be combined with the array sen-sors. Depending on the situation, the following centralisedsensors can be used: Fullbore and/or Inline spinner, NuclearFluid Density, Fluid Capacitance and Gas Holdup, plusGamma Ray/Casing Collar Locator, Temperature and Pres-sure.

In order to attain a good data set that can be processedfor quantitative results, Gysen et al (2010) came up with aset of recommendations about the tool string:

. Pay attention to water trapped in the hold-up tool probes.Trapped water may render the probes useless.

. Use the nuclear density tool with the array tools. The arraytools need correction for the irreducible water hold-up(IWH). This is possible with the density log.

. Use the full bore spinner with the array tools. The arraytools need corrections for the velocity reduction near thepipe wall. This is only possible with the full bore spinner.

For this particular job, the tool string consisted of SAT,Fullbore Spinner, RAT, Nuclear Fluid Density, Gas Holdup,Gamma Ray/Casing Collar Locator, Temperature and Pres-sure.

SAND DETECTION

A third party sand detection sensor combinable withthe array toolstring was run on trial in the Mungo well. Theresults from this tool are not the subject of this paper. How-ever, some interesting observations can be mentioned, basedon the internal temperature response from the SAT (Appen-dix 2).

During clean up (after shut in) and stabilizing, the wellproduced a significant volume of sand at surface (10% ofliquid production at its peak, see Figure 8)

As it can be seen in Appendix 2, some of the internalarray temperatures (from SAT) increase exactly at the pointswhere most of the water breaks into the well. This can beeXplained as internal friction increase due to sand productionat the specific location of the minispinner.

RESULTS

Shut-in data have been affected by sand present in thewell (horizontal section with perforations in green- Figure 9).

After clean-up and stabilization, two flowing rates werelogged, both with continuous passes and stations between per-forations.

Although not used in quantitative analysis, the station-ary data were used for QC and to check the consistency ofsensor response. Appendix 3 represents the SAT station @12% choke size. It shows the position of spinners relative tothe high/low side of the hole. SPIN1 position is given by theSATROT (tool rotation). The full bore spinner did not work




during stations (blocked with sand). Despite sand produc-tion, the array data is very stable. Appendix 4 presents theRAT data over a 300 seconds station above the bottom per-foration (3300 m). Note that probes 1-6 are located in waterwhile probes 8-11 are in oil. Probe 7&12 are located at theoil water interface. Rotation represents the location of probe1 in the pipe cross-section. DENR (red) is the centralisednuclear fluid density and reads water (1.01 glee). This RATdataset can be used for in-situ calibration in advanced inter-

pretation.Flowing data clearly show the difference between down

passes or LWT (logging while tractoring) and the up-passes.Appendix 5 shows the LWT passes while Appendix 6 showsthe up-passes for SAT and full bore spinner data. FlowingRAT data are presented in Appendix 7 for LWT passes andAppendix 8 for up-passes. Note the rotation effect on the up-passes.

All data have been analyzed using aprobabilistic approach(Gysen et aI, 2010), with both datasets at 12% and 15% chokesize indicating the water entry zones. Appendix 9 illustratesthe results from data acquired at 12 % and 15% choke size.

Despite adverse conditions, all sensors worked during theflowing passes, identifying the water entry points and meetingthe objectives of the survey.

CONCLUSIONS

This was the first LWT job with this type of array sen-sors in the North Sea. Despite adverse conditions, the sen-sors proved reliable and delivered good quality data.

Conventional centralised PL sensors have limited appli-cation in horizontal and highly deviated wells where fluidsegregation occurs and several different flow regimes maycome into play along the logged interval. The standard fluididentifiers, fluid density and capacitance, are usually centre-measuring devices and will not be reacting to representativefluid mixture samples; spinners are likely to show apparentdown flow as hydrocarbons flow on the high side of the welland displace water. The Temperature tool and the GHT(Gas Holdup Tool) will be useful, as their measurements arenot restricted to the centre of the well bore.

However array and centralised sensors do complementeach other when used in quantitative analysis.

If only water holdup is required, then array resistancewill suffice for an accurate quantification. (No need foranother array hold-up i.e. CAT in this case).

It is important to account and correct for the array sen-sors rotation along the well trajectory.

ACKNOWLEDGEMENTS

The authors would like to thank Murphy Petroleum,Total E&P, Nippon Oil Exploration, GE Oilfield Technol-ogy and BP for permission to publish this paper. Personal

April 2012

thanks are extended to Don Mitchell from Welltec, RodneyHoward and Marshall lung from Halliburton.

REFERENCES

Al-Buali, M., Dashash, A., El Gammal, T., Arevalo, F., Tome, 1., 2010.

Intelligent sensors for evaluating reservoir and well profile in hori-zontal wells: Saudi Arabia case histories, Paper 137202-MS: Soci-

ety of Petroleum Engineers, presented at the Canadian Unconven-tional Resources and International Petroleum Conference, Calgary,Alberta, Oct. I 9-21.

Frisch, G., Dorffer, D., and lung, M., Zett, A., Web- ster, M., 2009.

Improving the process of understanding multiprobe production log-

ging tools from the field to final answer, Paper 125028: Society ofPetroleum Engineers, presented at the SPE Annual Technical Con-ference and Exhibition, New Orleans, Louisiana, Oct. 4-7.

Frisch, F., lung, M., Alldredge, P., Zett, A., Webster, M., 2009. Provid-ing accurate PL interpretation with mult probe, multi-Sensor toolsin segregated flow environ ments, Paper M, in 50th Annual LoggingSymposium Transactions: Society of Petrophysics and Well LogAnalysts.

Gysen, A., Gysen, M., Zett, A., Webster, M., Yusti, M., 2010. Produc-tion logging in highly deviated and horizontal wells: moving fromqualitative to quantitative answers, Paper 133479-PP: Society ofPetroleum Engineers, presented at the SPE Annual Technical Con-ference and Exhi bition, Florence, Italy, Sept. 19-22.

Ryan, N., Hayes, D., 2001. A new multiphase holdup tool for horizon-tal wells, Paper V, in 42nd Annual Logging Sym posium Transac-tions: Society of Petrophysics and Well Log Analysts.

Zett, A., Riley, S., Webster, M., Brackenridge, R., 2008. An integrateddata acquisition and analysis approach decreases saturation uncer-tainty and provides valuable secondary information in difficult con-

ditions, Paper QQQ, in 49th Annual Logging Symposium Transac-tions: Society of Petrophysics and Well Log Analysts.

ABOUT THE AUTHORS

Adrian Zett is the Production Petrophysics Advi-sor in BP. His current role within Upstream Technologyis to lead and develop Production Petrophysics globallyin BP. His activity spans over 24 years in various Pet-rophysical roles. He holds a MSC in Geophysics anda PhD in Formation Evaluation from the University ofBucharest. He is a member of SPE and SPWLA.

Mike Webster is currently BP Distinguished Advi-sor for Petrophysics. He has been with BP for over 29 I

years including po stings in the North Sea and Alaska ina variety of mainly mature field and production petro-physics roles. He holds a BSc in Geology from Aber-deen University, a MEng from Heriot Watt UniversityEdinburgh, and is a Chartered Engineer.

Alwin Noordermeer is a Reservoir Manager with BPworking in Iraq. He has been with BP for over 16 yearsincluding postings in North Africa, Azerbaijan, Rus-

PETROPHYSICS 77



sia and the North Sea in a variety of field and petroleumengineering roles. He holds a MEng from Heriot WattUniversity Edinburgh.

Hugh Browne is a Manager with Downhole Measure-ments in Weatherford. He has over 35 years experience inwire line logging and interpretation in various roles withGE, Sondex, Schlumberger, Tesel, Gearhart, Halliburton,Sapesco and Geoservices. He holds a BEng in MechanicalEngineering from Bristol University.

Mitchell Hockley is a Senior Software Engineer with GEOil & Gas, Sondex Wireline. His current role is in develop-ing various software solutions to a number of downhole mea-surement issues and complete applications for the 2D and 3Dvisualization of downhole conditions. He has over 15 yearsexperience developing software for technical engineeringtasks. He holds a PhD in Physics from Reading University.

Charles Donkin is a Principal Engineer working forGE Oil & Gas, Sondex Wireline. His present role isto invent techniques and mechanisms for the develop-ment of innovative downhole sensors. With eight yearsexperience in wire line technology, his background is inthe introduction of new technologies, particularly in theData Storage Industry. He holds a BSc in Mechani-cal Engineering from Bristol University and is holder ofnumerous patents.

Glynn Lockyer is currently the Europe and Africawireline sales leader for GE Oil & Gas (Sondex Wire-line). He has over 12 years experience developing Pro-duction Logging software applications and systems. Hewas involved in the U-FL T Openhole development pro-gramme and helped design the Sondex memory system.He holds a BEng in Mechanical Engineering from theUniversity of Exeter.

78 PETROPHYSICS April20I2



Appendix 1 - MAPviewexample

90

JP~ I

'""wc,,'":~;;" "" I'''" WC'm,m

~- H""-"""-"'--H""

A B c DMD(m«."

,,500

'''OH4.5"Uno<

~

~~;~a~~e]onI! 11 i

..:~

,I

~

~p

90

3250

90

3300

89

Appendix 2 - Use of internal temperature from SAT for sand detection

Tracks A & B: 5 m/min Temp Array from SAT on 4 degF scale span (LWT and up passes)Tracks C & D: 12 m/min Temp Array from SAT on 4 degF scale span (LWT and up passes)The increase in array T correlates with the high water entry points, and could be caused by sand productionincreasing the friction of the local probe (minispinner)

April 2012 PETROPHYSICS 79


80


i

~', ~ ..............'"

0 0 0 00 0 0 0. 0 0 ., > > >. , . ,L L L £0 0 0 0, , , ,

~-~ vvvvvvv~ ~ ~ ~ ,..,, vvvvvvvI0" "~

~IroO ~c

~~,~" "

--Q~~'!'-"

00NPI

00PIPI

0'"NPI

Well completion and deviation

-"',..(SE<) rpo20 I 0rps rps 20 I 0rps

Station # 3175 m

,..(Sff) ?If ?Il n ~nros ros ~'nros ros ?nros

Station # 3230 m

""..(Sff) lOrDs 20 lOrDs 20 I on ---,p,--2OTO

--ruu- i150200250300

~O ;

ro' 201'0..~.I\.V'~.1; 3'60'

Station # 3300 m

Appendix 3 - Station SAT@ 12% choke size

PETROPHYSICS April 2012

150 I i200250 ! I

I300 !350

':

150 i !200 i i

i I .250 i I I300

.350 i

"



0.4215 0.9778 1.0119

~~,m,

77.801

200 -

lSO -

300 -

3SO -

Log plot ofRA T data over 300 second station at #3300 m. (From left: RATl-12, RAT rotation, Fluid Density)

Histogram ofRA T data over 300 second station at #3300 m.

Appendix 4 - Stationary RAT data above bottom perforation


0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.23000 .3000

.\2500 I II II I' F2500'I

ir2000

1,1' ,Il 'r;c'I Ii "or'r;

II Ir\\£

c

tr\1500

or>

I \, '"".....II '"

, '\ :;1000 ..I ii 1\E"u

'", I ,,\ "'500

00.2 0.3 0.4 0,5 0.6 0.7 0.8 0.9 1 1.1 1,2

Variables:

RATrNol: [-4.8 - 417.7] RATMN07: [-4.8 - 417.7]

RAnN08: [-4.8 - 417.7]

RATI"H'W9:[-4.8 - 417.7]

RATMN10: [-4.8 - 417.7]

417RATi'1f'.I06:


MD ~'

(met,,)1,500 "

3200 -I

3250

3300

Adrian Zett, Michael Webster,Alwin Noordermeer, Mitchell Hockley, Glynn Lockyer, Hugh Browne, Charles Donkin

''''0 c,>"""

'n' "'" ,"00""

00"",;orIT

I

I)

~.

"'".""'"." "n

"m,c"

"'""To

"'"'" "T'0'

"""

'C' 'no

c'"'~,

"TO c' """""' ,,',

-----

c"'"

"

ft

J

cO' "T'

1

~

1~(

c,,> "To

~} )

c,> N

"

0

~"-

~ c;

From left: SAT rotation, Array Spinners 1-6, Fullbore Spinner, Well Trajectory and Well Completion

Red down @5 m/min; Blue down@12 mlmin; Light blue down@12m/min repeat

Appendix 5 - Flowing spinner data LWT (loggingwhile tractoring / down passes)

82 PETROPHYSICS

\

W'''',"'m''',

8.5"OH

P,"

7"Uee,

)~~"

6" OH

Pe" I jf !

\ -! 1H45"Un"

V'! j! I

April 2012



Appendix 6 - Flowing spinner data (up passes)


... ""...'"

--- -

,moo ""n """ "'" '"'' "'" '"'' ,eo, "'" '0> "" "" "" '0> "" '" '" '0' '" '" "" '0' "

MD(m."" ""00 ""n """ "'"' "'"' "'"' '''"' ,eo,,500 , '"'" 0> '" .. "" .' w, .. "" >'" ., '" .' "

<;2 <1') \) \

)8S'OH

s:'

<

)

)

I' , <

3200 Pert

,::-/

<<0 < \.

j7"

f'

Lln >..II1II .<-<: " r,,-- "-

/, 6"OH

j))?

Pee'I !

3250 "/- <

1//<' '"

( <J <I If I ( ;

II /

i) ) f # r [f -I I;//( 1\

1 f.

3300(

(

( '" 4.5"

\ I Sv

Une,

i

!J

I

j\

"

-j1j \iFrom left: SAT rotation, array spinners 1-6,fullbore spinner,well trajectory and well completion

Red up@5 mlmin; Blue up@12 mlmin; Green up@20m/min



,m,'""

I:I Ii i

i II I! !I!

III IIiI I, IIIi i

I-iI IIiI II"

1\

i~F

,t~Lf,I-i

I

!I

-kf.£::..

ii~!~1-,rrI

r~

,iJ!!!;-4~\-~!

~~[

From left: RAT rotation, array resistance 1-12, nuclear fluid density, well trajectory and well completion

Red down @5 m/min, Blue down @12 m/min , Light blue down @12 mlmin repeat

Appendix 7 - Flowing RAT data LWT (logging while tractoring / down passes)

I'" i I Ii~. I \i) I

)i

\ . ,I )1

-j I( \1;~,( (i

.' ,'I

,.< I r II

iII

lj'I

IIII'III

"m"'"'"' "f,,--"""'--<,

From left: RAT rotation, array resistance 1-12, nuclear fluid density, well trajectory and well completion

Red up@5m/min; Blue up@12m/min; Green up@20m/min

Appendix 8 - Flowing RAT data (up passes)

84 PETROPHYSICS

,i !I

!' j

April 2012



,;:;:;,

')

'I..40~ I

/._1

)~ "

-- I~,~=",,,.,~ --."/

I

~'

I ;~Ii

1M ~

'S'~"

,.~I~

1MI

M)

r'1:""~

I

.;;.

~

,j~I

j

(J?

Appendix 9 - Production loggingquantitative results

Track 1 - water, Track 3 - oil at 12% choke sizeTrack 2 - water, Track 4 - oil at 15% choke sizeTracks 5, 6 - flow profiles at 12% and 15%



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