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NewTechnology

November 2011

Into Hot WaterThough a tough sell, geothermaldevelopers making inroads in oil country

Water Over Oil

Chemical helps produce coldheavy oil too thick to pumpat economic rates

the frst word on oilpatch innovation

PUBLICATIONSMAILAGREEMENTNO

.40069240

OptimizingOptimizatiOn

New software technologyis revolutionizing

reservoir simulation

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NewTechnologyMagazine|November2011 15

reservoir optimization

Images:SPTGroup,

SchlumbergerandChevronCorporatio

n

A new sotware technology

is revolutionizing reservoir simulationBy Gordon Cope

otz

otztoThe frst solution to a reservoir simulation problemis not necessarily the bestthere may be multiple,equally valid ways o history matching simulationmodels. Rather than stop at the frst viable solution

ound, optimization will provide a number o versions

o the modellike parallel universesand determine

the optimal o those potential solutions.

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16 newtechmagazine.com


Every year, nding and producing

oil gets more difcult and moreexpensive. Petroleum companiesspend billions of dollars drillingremote prospects and developingnew ways of bringing crude to sur-face, but the only sure bet is thatthe nancial risks are alwaysincreasing. Companies spend a lotof money getting reservoir infor-

mation, and they spend a lot todevelop reservoirs, says Greg

Walker, a senior reservoir engineer with TalismanEnergy Inc., Southeast Asia operations. One ofthe big questions is always: Do we have a project wecan sanction, given the uncertainties? At the otherend is the question: Can we make operations as ef-cient as possible?

Over the last several years, a new software tool hasemerged that is not only taking some of the uncer-tainty out, but also improving the chances of success

and reducing the workload for geoscientists and engin-eers. Optimization software costs money, but itsquick and efcient, says Walker. As an example, wewere asked to develop well targets for an offshore eld.The rst time we did it, it took two months to picktwo well targets. Using optimization software, the sec-

ond time it took us one month to pick ve well targets.Thats a tenfold increase in efciency, and [that makesthings] a lot more interesting for the engineers as theycan spend more time on investigation rather thanshepherding data through dif ferent, separate pieces.

Optimization software works hand-in-hand withthe simulation software that allows engineers to

model reservoir geology, potential oil and gas output,and the most efcient surface facilities, pipeline net-works and other infrastructure needed to produce aeld. There is a lot of work going on in reservoiroptimization right now, says Tony Settari, a profes-sor in the chemical and petroleum engineeringdepartment at the University of Calgary. It is veryvaluable. It gives you more certainty.

Simulation software has been around for severaldecades. Some of the larger oil companies have

developed proprietary in-house systems, but themajority of rms use vendor software ranging fromsimple stand-alone programs to integrated suites

marketed by international service companies likeSchlumberger Limited and Halliburton.

Regardless of the origin, most reservoir simulationmodelling tools work in the same manner. They allrely on geological data (such as well core and logs and

seismic) that geologists and geophysicists use to cre-ate a static reservoir model of the eld. The produc-tion engineer then uses the reservoir model to createa production plan that will give a protable net pres-ent value to the operator. Information during the ini-tial exploration stages is often scarce, however, sogeoscientists and engineers rely on experience andjudgment to make reasonable assumptions regardingvariables such as permeability, porosity and faulting

that might exist between data points. If preliminary production data is available,several history-matching models can be run to compare actual production to what

the model predicts. If the t is poor, the engineer can go back and manually mas-sage the geological model until he achieves a reasonable match.

BT- Manual-simulation modelling has several shortcomings, however. First, it is verylabour-intensive, often tak ing several weeks to choose reasonable assumptions foreld variables. Secondly, even simple models require complex computations thatcan take dozens of hours to run. As a result, the engineer must weigh time andresources against likely outcome; in the end, relatively few models are run for anyone eld. The problem is, there are many non-unique solutions to the model,and the engineer may not have found the best one, says David Millar, seniorvice-president, reservoir optimization, for SPT Group. So as soon as he gets areasonable match, the project is done.

Oil companies were aware that simulation models gave a best-guess solutionwith an unknown margin of error, which tended to raiseskepticism regarding their value. They wanted a way ofreducing risk and uncertainty when it came to planning wells,facilities and pipelines. Building upon academic research atpetroleum engineering and mathematics schools around theworld, BP plc, Chevron Corporation and other majors beganexperimenting with probability optimization theorems to

improve the quality of simulation modelling.Prior to joining Talisman, Walker spent several years

working with BP to develop and deploy Top Down ReservoirModel (TDRM), their proprietar y optimization software.

BEST OF THE BEST

Black spheres on

the plots represent

possible (local)

solutions, but the

larger white spheres

represent the best

(global) solutions.

The engineer might

find the local

solutions and be

satisfied, but

optimization showsthere exist other,

better, solutions.

Images:SPTGroup

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For the companies, the attraction is linked to nding

new targets, and as a general rule there are a lot moresmall targets than there are big ones, he notes.

In turn, that forces two changes to the workows; weneed a way of getting very accurate predictions and test-ing a lot of small targets to conrm they exist. Most[manual] production optimization modelling projects sixmonths into the future, and has a 1015 per cent uncer-tainty factor. To get optimization to work, you need to

generate more than 100 reservoir models and reduceuncertainty to one to two per cent.

Optimization relies on a variety of algorithms, or complex mathematicalequations; the choice of algorithm depends on the task in hand. A simpleoptimizer is the gradient method, mimicking the rolling of a marble on anuneven surface to nd the nearest low point. More powerful techniques arethe genetic algorithm and evolution strategy methods, which mirror the pro-

cess of evolution where the best of each generation are used to father subse-quent generations, says Millar. The best of these children then, in turn,

are used to create the next generation, resulting in a steady improvement inthe quality of the solution.

SPT Group was established in Norway several decades ago to create mod-elling software that would help predict multi-phase ow from subsea elds.It markets a suite of simulation modelling software that is widely used in theoil and gas sector, including OLGA (multi-phase pipeline and wellboretransient modelling), FORGAS (reservoirs, wells, surface facilities andpipelines) and PIPEFLO (multi-phase steady-

state pipeline network design). The companynow has over 1,000 customers worldwide andsales approaching US$100 million.

In the early 2000s, SPT Group begandeveloping Multi-purpose Environment forParallel Optimization (MEPO), a softwareprogram originally developed for the nuclearindustry. The company tweaked the programto automatically evaluate a large number of

geological variables in order to create a rangeof potential outcomes. Last year, they launched a commercial version thatworked with FORGAS to allow engineers to evaluate a multitude of variablesquickly and efciently in order to create a range of potential production out-comes. Originally, MEPO was used with reservoir modelling, says MonaTrick, advisor, SPT Group Canada Ltd. Last year, we launched a model thatcan handle any E&P [exploration and production] function, from O&G[oil and gas] reservoirs, to wells and surface facilities and pipelines.

AATAOptimization benets oil companies at several levels.

First, it allows companies to explore a wide range of res-ervoir geological scenarios, from the best to the worst,and pick the most favourable ones. This allows you tobracket your space of uncertainty, says Jeffrey Yarus,manager, Earth Modelling, Halliburton LandmarkSoftware and Services (which markets the DecisionSpacesuite of interpretive tools).

Secondly, it allows quick and efcient matching of

historical eld data to the simulation model in order topredict future production. History matching can beconsidered as a bridge between the reservoir modellingand reservoir simulation, says Marko Maucec, researchfellow, Halliburton Landmark. Manual history match-ing is usually performed on a single reservoir model.This will give an idea on how to locally adjust the owproperties of the model, but it doesnt take into accountthe model uncertainty. Moreover, such manual history-

matched and adjusted models are frequently not suitable

for reservoir production forecast.A computer-assisted history matching will generatemultiple statistically equally probable models in order tocapture uncertainty, says Maucec. Because historymatching is a mathematical inversion process, it cannever give an exact match, but it can minimize thedifference of observed and calculated data. You can intel-

ligently select the representative models that account fora reliable reservoir production forecast and capture, aswidely as possible, the model uncertainty space.

Even with optimization, however, the old adage,Garbage in, garbage out still applies; if geoscientistsand engineers start with a bad set of assumptions, they

can waste weeks chasing down the wrong alley. To thatend, software companies have added preview functionsthat allow quick computations to see if users are in

the ballpark.You dont have to run full physics

reservoir simulations in optimizationand uncertainty loops all the time,says Dayal Gunasekera, reservoirengineering marketing manager for

Schlumberger Information Systems (which markets thePetrel and ECLIPSE suite of simulation modelling software).

You can capture the behaviour of the full model by

creating a simplied proxy. You run the proxy a few timesto get a surface response, then you can run thousands ofevaluations of different geological scenarios and dynamicdesign congurations. This really speeds up the process.

Global optimal solution

False optima

Stagnation region

Starting point

Sub-optimal (local) solutions

OpTimal

SOluTiOnS

Engineers tend to

stop once they have

found just one

solution, while

optimization can

rapidly identify

multiple solutionsto a range of

engineering

problems before

arriving at a global

optimal solution.

Image:SPTGroup

Images: SPT Group

mulTiplE wayS

OF lOOking aT

THE daTa

Optimization

software can

quickly examine

hundreds of

scenarios using

multiple data

sources to narrow

down the bestpossible outcomes.

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18 newtechmagazine.com


Optimization has been used in many different upstream-to-midstream situations. For instance, a small gas eld inAlberta had four wells producing gas at low pressure. Inorder to make the eld economical, the operator had to

choose a production scenario with the best net presentvalue. The most important variables included the number

of inll wells, connector pipe size and compressor power.Ordinarily, the production engineering departmentmight spend two weeks running simulation models tond an adequate NPV [net present value], says Trick.In half a day, MEPO analyzed over 100 scenariosand came up with an NPV range with a worst case of

$13 million and a best case of $27 million.In another case, an operator was looking to expand a

North Sea eld, and had done a traditional trial and errorreservoir simulation that indicated the eld needed sixnew wells. SPT Group offered the company MEPO on aone-month trial basis. The software quickly determinedthat the eld had a high probability of producing just as

much oil using only ve new wells. It saved the companyUS$25 million, says Millar.

BP has published a plethora of casestudies on TDRM. They note thatthe typical BP engineer performedan average 300 simulations per yearprior to TDRM; each engineer nowperforms an average of 100,000. Thecompany estimates that the systemhas added 20 per cent net present

value to its reser voirs.

BPs Azeri Field in the Caspian Sea had nine steeplystacked reservoirs, some open, some sealed. There werefew wells and poor seismic, and huge uncertainty overwhat production plan to pursue. Traditional ne model-ling took 240 hours per iteration. The company used

TDRM, and in two days had thousands of simulationcases that showed the best production prospects weresealed reservoirs with well dened gas/oil contacts;engineers were able to focus their production drillingprogram on that aspect.

The Teak Oileld in Trinidad had been operating for30 years, and it was time to drill inll wells. Traditionalmanual history matching showed three locations could

add two million barrels each, but TDRM was able todifferentiate uncertainty and show well location #2 had

the greatest certainty, i.e. least risk. It also showed wherethe need for more information was highest. It improvedcertainty and reduced the work cycle by 90 per cent.

BP was exploring a carbonate reservoir in the NorthSea. Manual history matching gave poor results, due to

over 80 variables. TDRM got a good match in less thanone month, and showed 50 million to 150 million morebarrels present than the manual match.

Optimization can also be used on pipelines. OLGA isused all over the world to develop multi-phase offshoreand onshore pipeline systems, says Trick. Its very goodat determining if problems might arise. MEPO works with OLGA to dohistory matching of measured pressures and temperatures and optimization,

nding the best combination of pipeline size, ow rates and other variables.The engineer can quick ly evaluate a range of pipe roughness values, sur-roundings temperatures, heat transfer coefcients and oil viscosity values tosee the effect on predicted upstream pressure.

The Shtokman eld is located 560 kilometres offshore Russia in theBarents Sea. It contains an estimated 3.8 trillion cubic metres of gas and 37million tonnes of gas condensate. Since its discovery in the late 1980s, variouscompanies have devised numerous development plans. The current operationsgroup (a joint venture between Gazprom, Total and Statoil) hopes to have the

eld under production by 2016. The plans include a oating production platformand a 560-kilometre two-phase pipeline system. Severe Arctic weather andsteep ocean bottom topography add a great deal of complexity to operating thepipeline, however, including hydrate buildup, maximum allowable ow, mini-

mum allowable ow, temperature, pressure and liquid buildup. Shtokmanoperations group used OLGA and MEPO to run uncertainty analysis on asmany variables as possible. They discovered that the most critical parametersto successful operation were pressure drops and uid buildup.

PRBOptimization is not without its shortcomings. One of the dangers of tradi-

tional reservoir modelling is that it can give you a false sense of security,says Millar. When youre extrapolating a few wells over many miles,there may not be enough information to make an informed decision usingtraditional simulation. We were asked by a company that was looking atbuying an offshore lease to help them out.

There were a few wells drilled, and the traditional simulation modelshowed that it had potential, but when we ran MEPO for them, they

BEST-CaSE

SCEnaRiO

Steam chambers

in SAGD

development,

designed

and optimized with

Schlumberger's

Petrel and ECLIPSE.

REduCing

unCERTainTy

A complex well

with multilaterals

and inflow control

devices, designed

and optimized

with Petrel and

ECLIPSE.

Image: Schlumberger Information Services

Images: Schlumberger Information Services

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Page 191/2 page horz

763102-76Cathedral Services Ltd.



realized that even a small percentage less in their

assumptions regarding porosity or permeability made iteconomically untenable. There was simply too littleinformation to rule out that probability. They ended upshooting some seismic, and the new information con-rmed poor reservoir quality in some areas. They endedup passing on the deal, and saved themselves a lot ofmoney, Mil lar says.

Industry has taken a pragmatic approach, in that

they see how a few types of reservoirs respond to severaltypes of algorithms, and haveused that information to experi-ment with optimization todevelop an empirical solution,says Walker. But there aremany different types of reser-voirs, and the industry may beblind to how those

reservoirs are grouped.Companies that arent familiarwith the techniques can easi ly

view the tools as a black boxthat isnt understood. With the

amount of value hanging on decisions, all it takes is one situation in which thetechnology is used incorrectly to ruin its reputation.

More engineers have to learn about optimization, to get an understanding

about what the optimization methods can do, and how to use the technology,agrees Trick. To that end, SPT has an academic licence program thatdistributes MEPO software f ree to universities with major petroleumengineering departments in North America and Europe.

FTRIn the near future, optimization will become more and

more common, not only because it saves engineers timeand improves results, but for nancial reasons. In thepast, the nancial departments in many oil companieshave been satised with one reasonable answer, says

Millar. Now, they are starting to demand to see arange of models, from worst to best, before approvingexpenditures.

Over the next decade, new wrinkles will be added.

Geomechanics are not included in optimization, says theUniversity of Calgarys Settari. When you look at elddevelopment in the Gulf of Mexico, for instance, your goalis to get as much out as fast as possible. But as the eld isproduced, it causes reservoir compaction and seismicityalong existing faults. You need to take that into accountand mitigate it, but it is not reected in the reservoir opti-mization. That will be the next horizon they will look at.

SPT notes that around 150 customers currently use

MEPO and sa les are growing strongly. We are veryoptimistic about the future, says Millar. Currently,optimization is applied to perhaps 10 per cent of O&Gmodelling scenarios. I foresee that, within a decade, themajority of modelling cases will use optimization.

COnTaCT FOR mORE inFORmaTiOn

David Millar, SPT Group, Tel: 403-277-6688,

Email: [email protected]

FlOw-THROugH

BEnEFiTS

Schlumberger

used SPT's MEPO

to optimize the

pipeline and

compression

system for a smallgas field in Alberta.

Image: SPT Group

n: a merging of diverse elementsinto a unifed whole

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