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20 Oilfield Review

Improving Well Placement withModeling While Drilling

Daniel Bourgeois

Ian Tribe

Aberdeen, Scotland

Rod Christensen

Oilexco North Sea Limited

Calgary, Alberta, Canada

Peter Durbin

Ikon Science Limited

Teddington, England

Sujit Kumar

Bogot, Colombia

Grant Skinner

Stavanger, Norway

Drew Wharton

Houston, Texas, USA

For help in preparation of this article, thanks to Adrian Kemp,Houston.

Drilling Office, ECLIPSE, GeoFrame, InterACT, Osprey,PERFORM, PeriScope, PeriScope 15 and Petrel are marksof Schlumberger.

Windows is a registered trademark of Microsoft Corporation.

Increased computing power, the growing capabilities of modeling and simulation

software, and human ingenuity across multiple disciplines are ushering in a new era

in reservoir management. The ability to update reservoir models in real time will lead

to exciting advances in wellbore placement, helping engineers and geoscientists

improve field development.

Sophisticated new LWD tools that help define the

reservoir are being combined with fast reservoir-

modeling software to optimize wellbore place-ment while drilling. This addition dramatically

augments the traditional uses of reservoir-

modeling and simulation tools, such as assessing

reservoir performance, forecasting production

and estimating reserves. Now, this combination

helps improve hydrocarbon recovery by showing

drillers where to drill more productive wells.

Furthermore, data acquired while drilling can be

added to the model to provide rapid updates.

Through the years, the E&P industry has

experienced the benefits of establishing a holistic

view of the reservoir. This view is reflected in

modern reservoir modeling and simulation soft-ware. One of the fundamental roles of these

software tools is to simplify the complex issues

regarding scale, data and uncertainty.

Post-stack seismic data used in modeling

define the interwell reservoir volume and charac-

teristics and represent a static snapshot of the

reservoir. Wellbore data from drilling and well-

logging operations provide detailed near-wellbore

information that can be interpolated away from

the borehole and across the reservoir volume.

Time-lapse, or 4D, seismic volumes are now

used to monitor reservoir changes through time,

examining reservoir dynamics. This often involves

the mapping of seismic attributes derived from

amplitude, phase and frequency content to high-

light changes in the reservoir from one survey to

the next.

Models and simulators help in assessing

and predicting reservoir performance and in

identifying production problems. Although the

terms model and simulation are often used

interchangeably, in the E&P business, there are

important differences between them. Models, or

conceptual models, attempt to represent actualsystems and are largely static, but can be

updated with new information. Simulators, or

simulation models, attempt to describe how a

system changes over time. Despite their

differences, both reservoir models and fluid-flow

simulators help engineers and geoscientists

develop successful drilling plans, make

completion choices, determine workover plans

and formulate secondary-recovery strategies.

The success of these applications relies on the

accuracy of the reservoir models.

In the last decade, drilling capabilities, along

with MWD and LWD technological advances,have largely outpaced the industrys ability to

manage and rapidly exploit real-time data in

modeling and simulation. Breakthroughs in

drilling include accurate bit placement using a

variety of new technologies, such as rotary

steerable systems coupled with advanced LWD

systems.1 Extended-reach, multilateral and

geosteering technologies have increased the

ability to contact more of the reservoir with

complex wellbores. Tremendous volumes of high-

quality data are now acquired with modern MWD

and LWD tools. Data can be transmitted to

surface and immediately sent to centers of

expertise for real-time interpretation. In many

cases, borehole placement could be further

optimized if the new information could be

quickly integrated into reservoir models while

drilling is still taking place.

1. Chou L, Li Q, Darquin A, Denichou JM, Griffiths R, Hart N,McInally A, Templeton G, Omeragic D, Tribe I, Watson Kand Wiig M: Steering Toward Enhanced Production,Oilfield Review17, no. 3 (Autumn 2005): 5463.

2. Bryant I, Malinverno A, Prange M, Gonfalini M, Moffat J,Swager D, Theys P and Verga F: Understanding

Uncertainty, Oilfield Review14, no. 3 (Autumn 2002):215.

3. Bratton T, Cahn DV, Que NV, Duc NV, Gillespie P, Hunt D,Li B, Marcinew R, Ray S, Montaron B, Nelson R,Schoderbek D and Sonneland L: The Nature ofNaturally Fractured Reservoirs, Oilfield Review18,no. 2 (Summer 2006): 423.

4. Ali AHA, Brown T, Delgado R, Lee D, Plumb D, Smirnov N,Marsden R, Prado-Velarde E, Ramsey L, Spooner D,Stone T and Stouffer T: Watching Rocks ChangeMechanical Earth Modeling, Oilfield Review15, no. 2(Summer 2003): 2239.

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Winter 2006/2007 21

Crucial to developing this application are

new software tools that enable a multidisciplinary

approach to model building and updating,

allow faster simulation using updated models

and help asset teams evaluate risk as models

and proposed well designs change with

new information.This article investigates advances in reservoir

modeling and simulation and their potential to

improve wellbore placement. The contrasting

roles of modeling in the past, present and future

are briefly discussed, along with the visionary

concept of simulation while drilling (SiWD). We

demonstrate how rapid model updating has

already helped operators place their wells more

successfully in the North Sea. Next, we describe

a recent test of SiWD capabilities and the

improvements that are required for further

advancement. Finally, we examine the potential

applications of real-time modeling and simulation.

Moving Modeling Forward

One of the many challenges in developing a

model is to strike a balance between the risk of

high uncertainty and the cost and time needed to

improve accuracy. When creating and main-

taining an optimized model, reservoir engineers

must consider data quality, quantity and

uncertainty. The timing

and frequency of incorpo-rating new data into a model

have an impact on the uses of models

and simulations such as forecasting

production, estimating reserves or planning

the fields development. For example, when

incorporating critical near-wellbore LWD data,

updates would need to be frequent to benefit the

drilling of the subject well. While the model is

being created and updated, uncertainty needs to

be assessed.2

Reservoir modeling occurs at many levels;

there are models within models. Geologic models

focus mainly on geologic-layer thickness,

depth and extent, but also include faultsa

source of reservoir discontinuity and compart-

mentalization. Seismic and borehole data often

provide the bulk of information from which to

build and update a geologic model, including

formation boundaries or layers. With data from

well logs and cores, petrophysical models

describe forma

tion lithologies and

reservoir propertiessuch as porosity, perme

ability and fluid content

This same information give

geoscientists an appreciation

of the variability within the

reservoir. As reservoir complexity

increases, for example, in naturally fractured

or heterogeneous reservoirs, the relationship

between porosity and permeability systems

becomes more difficult to model.3

As the industry moves to drilling in more

challenging environments, mechanical earth

models (MEMs) become vitalmost notably to

avoid subsurface drilling hazards.4 In addition

PVT models are used to portray fluid properties

across a range of phase-changing reservoi

conditions. These models require input pri

marily from laboratory measurements, so rapidly

updating this information in reservoir models

may prove difficult.

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Also, rigorous fluid-flow simulation helpsdescribe complex multiphase fluid-flow phenom-

ena in the reservoir. Production simulators also

consider flow behavior outside the reservoir, such

as phase slipping in the wellbore.

Reservoir models and simulators have

contributed to the oil and gas industrys improved

understanding and success in increasingly

complex reservoirs. Nonetheless, building, main-

taining and updating models are time-consuming

processes, which may involve numerous person-nel across several disciplines. Recent changes

in modeling methods and tools have made it

possible to update models while drilling to

influence drilling operations.

Contrasting Approaches

The established roles of modeling and simulation

are to predict reservoir performance, forecast

production and estimate reserves. Modeling and

simulation are also commonly performed to

determine completion and workover effective-

ness and diagnose productivity problems by

comparing actual production to predicted

production, especially in horizontal wells.

Moreover, fluid-flow simulation is crucial for

developing infill drilling plans and formulating

secondary-recovery strategies. While these

important tasks do not necessarily require rapid

decision making, accuracy is paramount to

reduce uncertainty. One way to reduce

uncertainty while drilling is to incorporate the

most recent information as quickly as possible.

To exploit new information while a well is being

drilled, improvements were needed in several

areas, including modeling and simulation

software and hardware, and MWD- and LWD-data

acquisition and delivery.

In the past, computer processor speed had

limited the ability to quickly and frequently

update models and run simulationsespecially

full-field simulation that often takes weeks of

computer and personnel time. Other factors haveinhibited model building and updating.

Throughout much of the 1980s and 1990s, the use

of MWD and LWD data in modeling was

inefficient, primarily because acquisition

technologies and modeling and simulation

software were not properly integrated. In

addition, the process often was not automated

and required human expertise.

Most models were built in discipline silos.

Some disciplines placed a higher priority on

modeling because they used models more

regularly and benefited more often from their

use. Drillers used models less frequently, andas a result, their models were not optimized

to solve issues related to drilling. This has

changed; a general lack of cross-disciplinary

integration has given way to the multi-

disciplinary asset-team approach, immersive

visualization of the reservoir and real-time data

delivery(above left).

Model Well Paths

The Brenda field in the North Sea produces oil

from a system of channelized turbidite sandstones

(left). Individually, its reservoir sands are

frequently too thin to be explicitly resolved by

seismic methods, complicating exploitation

efforts. Two 3D seismic datasets and information

from 13 wells were available for field appraisal;

these were used to generate a reservoir model.

Modeling fluid flow in the reservoir using

ECLIPSE reservoir simulation software suggested

a four-well development program would be

needed to optimize reserve recovery. To locate oil-

22 Oilfield Review

> Immersive visualization. Early visualization technologies were primarily used to interpret 3D seismicvolumes. Today, the emphasis is on collaboration across multiple disciplines for visualization in well,reservoir and field management, including well placement.

> Location of the Brenda field in the North Sea. Potential drilling targets inthe Brenda field were identified using an advanced seismic preprocessingtechnique, a high-resolution velocity model, prestack seismic imaging andelastic-impedance analysis. Three wells have been completed in the UpperBalmoral sandstones, and production-test results have been encouraging.A fourth well drilled into the reservoir has been cased and is awaiting thedrilling of the horizontal leg.

km

miles0

0 100

100

Aberdeen

Edinburgh

Bergen

Stavanger

NORWAY

UK

Brenda

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Winter 2006/2007 23

bearing sands within the field, the operator,

Oilexco, and Ikon Science used 3D seismic

processing techniques with prestack data,

including specialized elastic impedance compu-

tations and amplitude variation with offset

(AVO) analysis.

During 2006, Oilexco drilled three production

wells and started a fourth well in the Brenda

field. This four-well project targeted three

sandstones in the Paleocene Upper Balmoral

member of the Montrose group. In the first three

wells, the true vertical depths ranged from 6,000

to 6,500 ft [1,829 to 1,981 m], whereas the total

measured depths reached 13,700 ft [4,176 m].

Total reservoir thickness in these wells has

ranged from 40 to 60 ft [12 to 18 m]. The top sand,

the UB3, is usually of good quality but thin, and

presents a difficult target to hit and stay within

while drilling. The lower sand, the UB1, is also of

good quality but is sometimes below the oil/water

contact. The middle unit, the UB2, is thicker and

more shale-prone, and is not a primary reservoir

target everywhere in the field.

Seismic data were used to define the optimal

landing point from which to start the horizontal

portion of the wellbore. Given the challenging

reservoir target, the relatively low resolution

from seismic data, local dip variations of the

reservoir top and the long horizontal wells used

to exploit the reservoir, depth accuracy while

drilling was a major concern. More specifically,

depth errors between the bit and the model had

to be resolved prior to landing the borehole near

the proposed target. The targets were in areas

defined by seismic imaging as exhibiting low

elastic impedance, a good indicator of hydro-

carbon accumulations in the Brenda field.

To resolve depth errors, an Oilexco operations

geologist would establish the actual drilling

depths of two markersthe tops of the Sele and

Lista formationslying just above the Upper

Balmoral sands, then compare the drilling depths

to seismically determined depths and adjust the

drilling path accordingly. Oilexco required that

wells hit the reservoir while the wellbore

was nearly horizontal89 deviationto ensure

optimal location of the well path within the

reservoir. Immediately beneath the top of the

reservoir, the 1212-in. casing was set. A successfu

landing was paramount for the ultimate drilling

objectiveto maximize contact with the mos

promising reservoir. The horizontal leg was

drilled using an 812-in. bit.

As the wells were drilled, real-time borehole

survey data transmitted uphole were delivered to

Oilexco in Aberdeen, using the InterACT real

time monitoring and data delivery system, and

then sent electronically to Ikon Science in

London. These new data were incorporated into

the Petrel seismic-to-simulation software mode

so that the current bit location could be

displayed with respect to the desired targets in

the model. Using these displays, the operation

personnel in the Aberdeen Oilexco office could

send proposed well-path changes back to the rig

to optimize the landing of the well into the

reservoir in preparation for drilling the horizonta

portion of the well (below).

> Petrel well planning and visualization of Brenda field D3 well. The top map, used by the operations geologist for landing the well, shows the reservoir structurecontours in black and white. Areas of low elastic impedance are shaded in light blue. Existing wells are blue and show the top of the reservoir as orange dotsThe proposed D3 horizontal well path is shown as light green, and the actual D3 horizontal well path is in red. A Universal Transverse Mercator (UTM) grid onthe map allows direct manual plotting of well coordinates. The bottom image shows the proposed D3 horizontal well path (light green) in 3D, together withexisting wells (blue and red) that show formation tops. Pink spheres indicate the top of the Balder formation, light blue spheres show the top of the Seleformation, yellow spheres mark the top of the Lista formation, and orange spheres identify the top of the reservoir. The orange target boxes define the landingpoint and the XY limits of the horizontal path, and the white outlines surround areas of low elastic impedance that indicate a high probability of commercialhydrocarbons. These areas are draped onto the contoured 3D surface of the reservoir top. The arrow in the bottom right corner shows the north direction.

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Once the wellbores were successfully landed

in the top of the reservoir, Oilexco needed a more

precise way to evaluate the reservoir sandstones

and to locate the nonproductive shale immedi-

ately around the wellbore. To accomplish this,

Oilexco used the Schlumberger PeriScope 15

directional, deep imaging while drilling tool

(above). The PeriScope 15 tool is a deep-reading

electromagnetic resistivity device that

determines the direction and distance to bed

boundaries by showing conductivity contrasts.With a transmitter-receiver spacing of 96 in.

[244 cm], the tool has the theoretical capability

to detect boundaries up to 15 ft [4.6 m] from the

borehole. However, the actual resolved distance

depends on the resistivity of the surrounding

and adjacent beds and the complexity of the

geologic layering.

The PeriScope tool acquired data from

around the borehole and successfully identified

the reservoir ceiling and the presence of zones

of lower quality within the reservoir, helping

Oilexco to fine-tune the drilling of the horizontal

wellbore. Adjustments were then made in

steering the wellbore to maximize wellbore

length within high-quality reservoir while

maintaining the largest possible standoff

distance above the oil/water contact at the base

of the reservoir.

Azimuthal polar plots generated from the

PeriScope tool results show the bit position with

respect to nearby bed boundaries, allowing the

24 Oilfield Review

> PeriScope curtain section for the Brenda field D1 well. This curtain-section display of Brenda Well D1 was used by the geosteering team to optimizethe placement of the 1,800-ft [549-m] horizontal well leg (red, from left to right) in complex geology. It enabled them to stay predominantly within 10 ft[3 m] below the top of the Upper Balmoral reservoir. The lighter colors represent higher resistivity sandstone, and darker colors indicate lower resistivityshale. Without the PeriScope information, much of the proposed well path (blue-green) would have strayed into the shale, making the last half of thehorizontal leg nonproductive. The image also highlights a low-resistivity zone from 10,750 to 11,050 ft MD. Missing the reservoir in this section allowed

drillers to optimize pay-zone contact in the high-resistivity zones.

11.271.612.042.583.274.155.256.668.4420.70

13.5617.1921.7927.6135

Tru

eresistivity,

ohm.m

1040

0

1030

0

1050

0

1020

010

100

1060

0

1070

0

1080

0

1090

0

1100

0

1110

0

1120

0

1130

0

1140

0

1150

0

1160

0

1170

0

1

6,740

6,760

6,780

6,800

6,820

6,840

1,8001,600

Trueve

rticaldepthsubsea,

ft

6,820

6,840

6,860

6,880

6,900

6,920

6,9401,4001,2001,0008006004002000

Tru

everticaldepth,

ft

True horizontal length, ft

> Creating a mapable surface from PeriScope results. It is possible toconvert the PeriScope results that define the top of reservoir into a mapablesurface using the distances from the borehole to the bed boundariescalculated from the PeriScope tool and borehole-survey data. In thisexample, the data are used to create surface sticks (pink) representing theboundary identified by PeriScope readings. From this a surface is created(red with black contour lines). The surface is not shown in areas where itdips below the wellbore.

10000

11000

11500

0500

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Winter 2006/2007 25

geosteering team to make real-time trajectory

adjustments to optimize well placement.5 The

bed boundaries defined by the PeriScope images

could then be converted to Petrel surfaces and

mapped within the reservoir model (previous

page, bottom).

The use of Petrel software to model the

Brenda field facilitated rapid reservoir

mappingusing geologic and geophysical

dataand well-path design in one software

package running on a standard laptop PC. Petrel

software was instrumental in the Brenda field

drilling workflow because it gave everyone

involved the ability to observe the wellbore

position with respect to the reservoir prior to and

after landing, enabling efficient well-path design

changes, avoiding plugbacks and sidetracks, and

maximizing productivity.

Oilexco has completed three Brenda

production wells, which are currently being tied

into the Brenda manifold. The completion flow

tests for the first three wells exceeded Oilexco

expectations. Their sandface productivity indexesand normalized flow calculations suggest a

theoretical combined production rate of

44,000 bbl/d [6,995 m3/d] of oil.6 First oil is

anticipated in late 2006 or early 2007.

Operating companies around the world are

increasingly using Petrel software to visualize

the reservoir, make interpretations, evaluate risk

and rapidly update the model while drilling,

allowing them to optimize bit placement and

produce more hydrocarbons. Modeling-while-

drilling workflows have also been successful in

fields offshore Vietnam, India and Malaysia.

Initially, visualization referred to seismicvolume interpretation using high-power

computers and was not connected to reservoir

models. However, to fully understand reservoirs

and fields, a wide variety of data must be

analyzed and multiple disciplines must be

involved. With Petrel software, a more

comprehensive workflow is now possible using

low-end Windows PCs at the desktop and in

collaborative multidisciplinary environments.

This allows asset teams to visualize, evaluate and

assess complex relationships in 3D, and through

time to better understand risk and uncertainty in

multiple scenarios and more accurately predict

production behavior.

Modeling Ahead

Specialized tools within Petrel software are

tailored for modeling-while-drilling applications.

For example, the Petrel Process Manager

facilitates fast data loading and model updating

by establishing an automated workflow. This

reduces decision-making and cycle time, and

saves time and money. Well paths can be designed

and updated using the Petrel Well Design tool,increasing drilling efficiency and bit-placement

accuracy. The integrated workflow also can model

log responses ahead of the bit along the proposed

well path. Generating modeled petrophysical

responses ahead of the bit helps asset teams

understand the reservoir more fully and lets them

choose the optimal well path in 3D, reducing

uncertainty in complex settings.

While some of these capabilities are here

today in limited use, more widespread usage may

be imminent. Many advances have enabled the

move to modeling while drilling. For example,

supervisory control and data acquisition

(SCADA) systems, which have been in place for

many years, allow immediate access to downhole

data and control of downhole hardware. In

addition, the new generation of reservoir simu-

lators, which exploit faster, more sophisticated

processors, has increased the computational

power available to asset teams. Reservoir models

are now truly multidisciplinary tools that evolve

as new reservoir or field information is acquired

such as new 3D seismic volumes, well logs, coredata, well-test data or production-history

information. Petrel softwares unique structure

and functionality, coupled with its PC

compatibility, facilitate integrated workflows in

geology, geophysics, well engineering and

reservoir engineering (above).

Most reservoir models incorporate porosity and

permeability only within reservoir sections and

ignore the effects of overburden. MEMs contain

stress, mechanical rock-property and pore

pressure predictions from the reservoir to the

surface. Consequently, workflows frequently break

down when the MEM is inadequate or nonexistent

Knowledge of overburden geomechanics greatly

improves the well-construction process because, in

part, it allows asset teams to assess the risks along

a proposed well path and avoid hazards.

> From seismic data to simulation. In this example, Petrel software has beenused to visualize production data and perform history-matching, improvingsimulation and field development. The upper left image shows the reservoirsimulation model with local-grid refinement around the boreholes. Theupper right shows a horizontal well through the reservoir model with theseismic volume displayed in the background. The lower left shows thepossible production-profile output from the ECLIPSE reservoir simulator.

Each curve represents a different model realization created for anuncertainty study. On the lower right is a cumulative production plot at eachborehole. The bubble size represents relative production volume. The Petrelworkflow not only allows experts from multiple domains to meld theirdomain-specific information and knowledge into a single model-centricrepresentation, but it also supports the ability to easily update and visualizethe collective understanding as soon as new information is available. It isnow possible to visualize, evaluate and assess complex relationships in 3Dspace and time to better understand risk and uncertainty in multiplescenarios and to more accurately predict reservoir flow behavior.

5. Chou et al, reference 1.

6. http://www.oilexco.com/news/060622.pdf (accessedSeptember 29, 2006).

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In the past, exploiting the information in

mechanical earth and reservoir models, including

uncertainties, for practical drilling applications

has not been straightforward. However, in 2000,

Schlumberger Cambridge Research in England,

as part of the MoBPTeCh industry consortium

comprising Mobil Oil, BP Amoco, Texaco and

Chevron, completed the development of a

drilling-simulator prototype. This software lays

the groundwork for more automated risk

assessments in difficult drilling conditions.7

Today, the Schlumberger Osprey drilling risk

prediction software and a Petrel plug-in enable

critical risk assessment and drilling-cost and

drilling-time estimates, in addition to providing a

collaborative link between drillers, geophysicists

and geologists.8 Following an efficient workflow,

Osprey and Petrel tools allow asset teams to

interactively design well trajectories and to

update well-design plans as the model or

proposed well path is changed (left). Another

advantage of this software is that drilling

engineers can customize the system to incorporateregulatory and company requirements, as well as

local and historical experience.

The industry is now considering the

possibility of simulating reservoir response to

new wells while drilling them. Along with the

integration of real-time data into models and

rapid model updating, the E&P industry is also

benefiting from faster simulators. This is

especially important when simulating complex

fluid-flow and production behavior in large

reservoirs, because these require large reservoir

models. The need for dynamic evaluation while

drilling intensifies as complexity increases. Forexample, simulating while drilling (SiWD) in

three-phase, heterogeneous reservoirs already

affected by nearby producing wells would be

more beneficial than when drilling homogeneous,

single- or two-phase reservoirs with zero dipa

case where using field experience might suffice.

The idea of reservoir simulation while

drilling, or dynamic evaluation, is not new. One

such effort began in 1997 as part of a near-

wellbore modeling project by BP, Schlumberger

GeoQuest, Norsk Hydro and Saudi Aramco.9 This

early project determined that real-time

optimization of a well path is a true multi-

disciplinary exercise, requiring asset teams to

have a clear understanding of the common goal

and to be prepared for changing scenarios.

Another finding was that real-time model

updating should focus on the near-wellbore

volume, where MWD and LWD data are most

pertinent. Permeability distribution along the

wellbore is critical to the predicted well

26 Oilfield Review

> Saving time and money while reducing risk. Osprey Risk interactive software wasdesigned to assist asset teams with well planning by providing probabilistic cost, timeand risk assessment while incorporating geological and geomechanical models intothe process. With subsurface targets identified, the well trajectory is designed in thePetrel Well Design tool (top) and input into Osprey software as a deviation survey.Earth-model dataat a minimum, pore pressures, fracture gradients and unconfinedcompressive strengths from the Petrel MEMare also required input. The softwareproposes optimal hole size, bit type, maximum mud weights, and casing sizes, weightsand set depths, taking into account production requirements, wellbore stability andmany other factors. Using the available data and Monte Carlo simulation, drilling timesand costs are calculated at key depths for a set of defined probabilities (bottom). This

output can be used as a working operational plan. Significant drilling risks for thetechnical design are generated and can be displayed individually or grouped intocategories of fluid gains, mud losses, stuck pipe and mechanical problems. A total riskindex is also computed and can be used to rank scenarios. In the risk display, darkgreen indicates low risk, light green shows low to medium risk, yellow means mediumrisk, orange indicates medium to high risk and red shows high risk. Osprey Risk and itsplug-in are fast-responding applications so that drilling engineers and geoscientistscan easily change the design and compare results within minutes. This process leadsto reduction of technical risk and highlights where mitigation strategies will be neededto implement the operational plan.

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Winter 2006/2007 27

performance. However, accurately capturing

near-wellbore flow phenomena requires a

smaller grid size and local-grid refinement,

which decreases the processing time step while

increasing processing time. Also, full-field

simulation while drilling was deemed unrealistic

in most cases because of the imposed time

constraints during drilling operations. As part of

this work, software was developed that generated

a reduced near-wellbore model within the full-

field model and served as an early simulation-

while-drilling prototype tool.

Well Performance to Define Well Placement

In 2006, simulation while drilling (SiWD) was

defined as a real-time optimization process to

dynamically improve the design of the trajectory

and the configuration and completion strategy of a

well while it is being drilled.10 This concept has

become more relevant today with the emergence

of innovative drilling, MWD and LWD technologies

that have enabled geosteering and advanced wells

with elaborate trajectories, multiple branches orboth. However, one of the key drawbacks in

drilling these advanced wells was the level of

uncertainty inherent in the initial reservoir

description, including which fluids are present.

This uncertainty accentuated the need for real-

time data collection, integration and interpretation.

SiWD has not been adopted for several

reasons. The industry has only recently realized

the advantages of real-time MWD and LWD data in

well construction, reservoir and simulation

engineering.11 In addition, the seamless integra-

tion of while-drilling measurements into modeling

and simulation software tools has been difficult.Until recently, an integrated approach has been

hampered by the lack of a proper platform from

which multiple disciplines could work. Moreover,

there has been a perception that model updating

in the appropriate time frame was not possible.

Finally, to be feasible, complicated workflows

needed for real-time evaluation of multiple well

trajectories and configurations will require

automated optimization.

Today, geosteering involves the interactive

placement of the borehole based on geology and

the desire to contact as much of the reservoir as

possible, with the goal of optimizing the initial

well productivity. While this technique has been

successful, complicated scenarios require a more

rigorous approach to effectively reduce risk. With

todays computing power and the increased

ability to model critical factors while drilling,

experts are envisioning the possibility of

simulating well productivity ahead of the bit.

Critical factors that impact short- and long-term

productivity include well-completion options,

multiphase-flow phenomena in the reservoir and

in the well, the effects of drawdown at the

wellbore, and pressure and fluid changes in the

reservoir from neighboring production or

injection wells.However, large, multilayer models make it

difficult, if not impossible, to update, upscale

and perform full-field simulations in time to

impact simultaneous drilling operations. This

problem is tempered by the fact that it is not

essential to examine all regions of the reservoir

equally when evaluating the future performance

of a single well. For example, changes in

reservoir pressure or hydrocarbon saturation at

great distances or in isolated layers might have a

minimal effect on the subject well. There may

also be minimal effects when the near-wellbore

permeability distribution dominates the analysis

Here, semianalytical methods can provide fas

and accurate results when modeling unconventional wells, but are less rigorous

when dealing with multiphase-fluid flow and

reservoir heterogeneity.12

A closed-loop process was tested to design

optimize and configure advanced wells in

real time (above). To prove this concept

Schlumberger reservoir and software experts

along with Spectrum Consultores, started with a

model based on data from a North Sea field. With

> A closed-loop process for simulation while drilling. Defined by thefrequency of measurements and the speed of optimization, the cyclicalprocess includes data acquisition and interpretation, model updates,parameter changes and simulation until an optimal solution is determined;in the final step, appropriate action is taken.

Action Propose

Simulate

UpdateInterpret

7. Booth J, Bradford IDR, Cook JM, Dowell JD, Ritchie Gand Tuddenham I: Meeting Future Drilling Planning andDecision Support Requirements: A New Drilling

Simulator, paper SPE/IADC 67816, presented at theSPE/IADC Drilling Conference, Amsterdam, February 27March 1, 2001.

8. Givens K, Luppens C, Menon S, Ritchie G andVeeningen D: Geomechanics-Based AutomaticWell-Planning Software Provides Drilling DecisionSupport to Asset Teams, paper SPE 90329, presentedat the SPE Annual Technical Conference and Exhibition,Houston, September 2629, 2004.

9. Be , Flynn J and Reiso E: On Near Wellbore Modelingand Real Time Reservoir Management, paperSPE 66369, presented at the SPE Reservoir SimulationSymposium, Houston, February 1114, 2001.

Be , Cox J and Reiso E: On Real Time ReservoirManagement and Simulation While Drilling, paperSPE 65149, presented at the SPE European Petroleum

Conference, Paris, October 2425, 2000.10. Primera A, Perez-Damas C, Kumar S and Rodriguez JE:

Simulation While Drilling: Utopia or Reality? paperSPE 99945, presented at the SPE Intelligent EnergyConference and Exhibition, Amsterdam, April 1113, 2006.

11. Aldred W, Belaskie J, Isangulov R, Crockett B,Edmondson B, Florence F and Srinivasan S:Changing the Way We Drill, Oilfield Review17, no. 1(Spring 2005): 4249.

12. Wolfsteiner C: Modeling and Upscaling ofNonconventional Wells in Heterogeneous Reservoirs,PhD Thesis, Stanford University, California, 2002.

7/27/2019 p20_29

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the flux-boundary condition technique and local-

grid refinement, the original 600,000-cell model

was reduced to 30,000 cells, thereby simplifying

the model (above).

With a reduced-grid, near-wellbore model,

several different well-path options were simu-

lated over a six-year production period and

compared based on three predicted outputs:

water cut, oil-production rate and gas/oil ratio

(GOR) (above right). Next, using the chosen

optimal well path, the reduced-grid simulation

was tested using a single-processor computer

against the full-field simulation. Although the

reduced-grid simulation yielded a slightly higher

water-cut prediction over time, the predicted

cumulative oil production and GOR were

comparable (next page, top).

The study proved that SiWD is feasible at

typical North Sea rates of penetrationabout

200 ft/h [61 m/h], depending on MWD and LWD

operational requirements and BHA and bit

configurations. In this study using a North Sea

reservoir model, times for the various workflow

steps were determined to be acceptable for a

typical 10-day horizontal drilling operation in the

field. However, time estimates vary because many

of these steps are dependent on model complex-

ity and size and hardware and software

availability. Data acquisition and transmission

were assumed to occur in real time. The steps

included analysis and interpretation of the new

information; updating the model; gridding

optimization involving perpendicular-bisector

grid processing and local-grid refinement; the

initial new well proposal; and simulation runs

using the near-wellbore model. In this example

in a typical established field, the total estimated

turnaround time was 20 to 30 minutes.

Keeping It Real Time

From an engineering perspective, simplifying

models to enable modeling and simulation while

drilling is not necessarily the answer. However,

simplifying the workflow is always a positive step.

Software tools continue to become faster and

easier to use, connectivity to remote locations is

increasingly reliable and larger data volumes are

being transmitted at higher rates from downhole

tools to end-users as technologies improve.

28 Oilfield Review

> Making simulation faster. Grid-coarsening, local-grid refinement andboundary-conditioning techniques are used to decrease simulation timewhile preserving sufficient resolution of reservoir heterogeneities and

allowing multiple geostatistical realizations. In this North Sea example, thesimulation model of a channelized reservoir (top) is optimized by upscalingeach cell to a given resolution, which is defined by the local geologicheterogeneities, the degree of fluid-flow activity and the distance from thesubject well (bottom).

> Evaluating alternative well paths. For threeproposed well trajectories, three inflow-performance predictions were used to determinethe optimized well path: water cut (top), oil-production rate (middle) and GOR (bottom). Inthis example, the simulation of Trajectory 1-2

(blue) terminates early because the higher watercut has exceeded the assumed surface water-handling capabilities. Trajectory 1-1 (green) isoptimal because it shows the largest cumulativeproduced-oil volume and results in the highestnet present value.

01/04 12/04 12/05 12/06 12/07 12/08 12/09

Date

0

2

4

6

8

10

12

14

Watercut,%

01/04 12/04 12/05 12/06 12/07 12/08 12/09

Date

12,000

10,000

8,000

6,000

4,000

2,000

0

Oilproduction,

bbl/d

1,400

GOR,

ft3/bbl

1,200

1,000800

600

400

200

001/04 12/04 12/05 12/06 12/07 12/08 12/09

Date

Trajectory 1-1

Trajectory 1-2

Trajectory 2-2

7/27/2019 p20_29

10/10

Winter 2006/2007 29

Another indication that the implementation of

modeling and SiWD will increase is seen in the

growing number of resources dedicated to real-

time drilling solutions. Schlumberger Operation

Support Centers (OSCs), for example, are now

distributed worldwide to remotely monitor, model

and control drilling processes. These centers are

staffed with experienced personnel armed with

powerful software to help operating companies

minimize drilling risks and achieve their drilling

objectives in a collaborative setting (above).

While the results have been encouraging so

far, several areas need further work. Well-path

trajectory optimization would be improved with

the development of automated well-path

selection algorithms. For complete optimization,

more consideration of downhole completion

systems is needed to better simulate the inflow

performance. The increased use of artificial

intelligence techniques should continue to be

explored. There remains a need to couple fluid

flow and geomechanics in SiWD. In addition,

integrating surface and subsurface simulations

would improve the accuracy of production

predictions, although this would add a significant

amount of time to the process. Finally, more work

is needed before SiWD in fractured and othe

complex reservoirs becomes feasible. Modeling

dual-porosity and -permeability systems and the

complex interaction between fractures and the

matrix is challenging, even without restrictive

time constraints.

The advances in modeling and simulation

software and hardware, coupled with the E&P

industrys increased understanding of complex

reservoirs and complex wells, will create a

more fertile environment for optimizing wel

placement while drilling. MG

> Real-time workflow. Secure, real-time transmission of downhole data from remote drilling sites is accomplished using the InterACT system or third-partyservers (left). Experts at the Schlumberger Operation Support Centers (OSCs) use this timely information and specialized software tools to help operatorsmonitor and analyze crucial drilling, geological and geophysical data; avoid drilling hazards; hit reservoir targets; and remotely control drilling operations(center). Throughout the process, a wide range of data, including depth, time, operational and trajectory data ( right), are used to update models, runsimulations and identify the appropriate actions.

Rig sensors

Downhole tools

Wellsite dataacquisition,aggregation anddisplay

InterACTdata hub

InterACT hub

Schlumberger OSC

Depth dataGeoFrame

Petrel

Operational data

Time data

Trajectory dataDrilling Office

Specialist servicesNo Drilling Surprises

PERFORMGeosteering

Remote monitoringRemote control

Client asset team

> Comparison of SiWD results to full-field simulation results of Trajectory 1-1. The optimized, near-wellbore simulation produced results similar to that of afull-field simulation of Trajectory 1-1 production rates over six years. Initially, the reservoir volume described in the near-wellbore reservoir model hasenough energy to match the full-field simulation volume results. However, after the initial three years in the near-wellbore simulation, the absence ofpressure support from the full reservoir volume shows up as relative permeability beginning to dictate fluid movement.

01/04 12/04 12/05 12/06 12/07 12/08 12/09

Date

1

2

3

4

5

6

7

8

Watercut,%

0

1,400

GOR,

ft3/bbl

1,200

1,000

800

600

400

200

001/04 12/04 12/05 12/06 12/07 12/08 12/

Date

Full-field simulation

Near-wellbore simulation

01/04 12/04 12/05 12/06 12/07 12/08 12/09

Date

2.0E+07

0

Cumulativeoil,

bbl/d1.6E+07

1.2E+07

8.0E+06

4.0E+06

2.0E+06

6.0E+06

1.0E+07

1.4E+07

1.8E+07