geothermal reservoir works hoop proceedings

8/3/2019 Geothermal Reservoir Works Hoop Proceedings

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PROCEEDINGSSIXTEENTHWORKSHOPERMAL RESERVOIR ENG INEER ING

JEUIU~IY3-25, I991

SGP-TR-134-7


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DISCLAIMER

This report was prepared as an account of work sponsored by anagency of the United States Government. Neither the United StatesGovernment nor any agency Thereof, nor any of their employees,makes any warranty, express or implied, or assumes any legalliability or responsibility for the accuracy, completeness, orusefulness of any information, apparatus, product, or processdisclosed, or represents that its use would not infringe privatelyowned rights. Reference herein to any specific commercial product,process, or service by trade name, trademark, manufacturer, orotherwise does not necessarily constitute or imply its endorsement,recommendation, or favoring by the United States Government or anyagency thereof. The views and opinions of authors expressed hereindo not necessarily state or reflect those of the United StatesGovernment or any agency thereof.


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PROCEEDINGS, ixteenth Workshop on Geothermal R eservoir EngineeringStanfordUniversity,Stanford, California, January 23-25. 1991SGP-TR-134

The Economics of Heat Mining: An Analysis of Design Options andPerformance Requirements of Hot Dry Rock (HDR) Geothermal Power Systems

Jefferson W. Tester and Howard J. HerzogMassachusetts Institute of TechnologyEnergy Laboratory, Room E40-455Cambridge, MA 02139

AbstractA generalized economic model was developed topredict the breakeven price of HDR generatedelectricity. Important parameters include: (1)resource quality-- average geothermal gradient("C/km) and well depth, (2 ) reservoir performance--effective productivity, flow impedance, and lifetime(thermal drawdown rate), (3) cost components--drilling, reservoir formation, and power plant costsand (4 ) economic factors-- discount and interest rates,taxes, etc. Detailed cost correlations based onhistorical data and results of other studies arepresented for drilling, stimulation, and power plantcosts. Results of the generalized model arecompared to the results of several publishedeconomic assessments.Critical parameters affecting economic viability aredrilling costs and reservoir performance. Forexample, high gradient areas are attractive becauseshallower well depths and/or lower reservoirproduction rates are permissible. Under a reasonableset of assumptions regarding reservoir impedance,accessible rock volumes and surface areas, and massflow rates (to limit thermal drawdown rates to about10C per year), predictions for HDR-producedelectricity result in competitive breakeven prices inthe range of 5 to 9 cents/kWh for resources havingaverage gradients above 5OoC/km. Lower gradientareas require improved reservoir performance and/orlower well drilling costs.IntroductionThe HDR geothermal energy resource is associatedwith accessible regions of hot rock beneath theearth's surface that do not contain sufficient naturalporosity or permeability. Energy can be extracted bycreating artificial permeability using hydraulicstimulation techniques to propagate and open jointsor fractures. The resulting fracture network isconnected to a set of injection and production wellswhere heat is removed by circulating water underpressure from the surface, down one well, through thefractured zone, and up a second well (see Figure 1).

Electricity and/or process steam would then begenerated using the heated water in an appropriatelydesigned plant. This heat mining concept is closed-loop on the geothermal side so there are no effluents,thus minimizing the environmental impact of theentire HDR "fuel cycle" to site preparation, welldrilling, and other land use issues.Because HDR systems do not require natural,indigenous hot fluids and highpermeability, the HDRresource itself can be defined by the accessiblethermal energy in the earth's crust above someminimum temperature level. Thus the size of theHDR resource is very large and more widelydistributed than natural geothermal systems. Fo rexample, in the U.S., to a 10 km depth assuming anaverage geothermal temperature gradient of 2S"C/kmand a minimum initial rock temperature of 150C(deg C), the amount of thermal energy in place isabout 10 million quads (Tester, Brown, and Potter(1989)). The worldwide HDR resource base isestimated a t over 100million quads (Armstead andTester (1987)). Based on the enormous size andubiquitous distribution of the resource and its positiveenvironmental characteristics, HDR could provide anacceptable alternative to the fossil and nuclearoptions for meeting a substantial fraction ofworldwide electric power and space and process heatdemand.The main objectives of this study were first, to reviewand analyze several economic assessments of Hot DryRock (HDR) geothermal energy systems, and second,to reformulate an economic model for HDR withrevised cost components. This paper in large part isan extension of our earlier work on HDR economicforecasting (see Tester and Herzog (1990) for adetailed discussion of the methodology used). Theeconomic models reviewed include the followingstudies sponsored by:

Electric Power Research Institute (EPR1)--Cummings and Morris (1979)Los Alamos National Laboratory (LANL)--Murphy, et al. (1982)

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non-thetmal with gradients of about 20 to 25"C/km.About 16% of the land area in the U.S. can becategorized as a thermal area with a significantfraction existing in hyperthermal regions near orwithin active hydrothermal systems. A typical rangefor average gradients in such hyperthermal systemswould be from 60 to 8O"C/km. Fenton Hill, NM andRoosevelt Hot Springs, UT fall into this lattercategory.

i!515tK5 1058 5 -2I

-In

Figure 1. HDR reservoir concept for aninterconnected network of fractures stimulated in alow-permeability formation (from Tester , Brown, andPotter (1989)).

HIGH LOW

- DRILLING COSTS-

- United Kingdom (UK)--Shock (1986) with anupdate by Harrison, et al. (1989). Japan--Hori, et al. (1986). Meridian--Entingh (1987)Bechtel (1988)Geothermik--Smolka and Kappelmeyer (1990)

Before discusssing the predictions of these models,HDR resource and performance parameters arereviewed.

Key Resource and Performan ce Param eters

Although HD R reservoir temperatures are selectedas P design choice, an acceptable range can easily bebracketed for electric power applications. In anysituation, one balances the cost of producing the fluidagainst the cost of converting its thermal energy intoelectric power. Effectively, this is equivalent tobalancing drilling costs against power plant capitalcosts to reach a minimal total cost corresponding tooptimal design temperature or reservoir depth for aparticular HDR site. These effects are illustrated inFigure 2. Using the dashed line for reference, onecan see that reservoir design temperature range fromabout 140C for low gradient areas (20C/km) toabout 250C or more for high gradient areas(>8O"C/km) with a fairly flat minimum. Strictlyspeaking, the actual values of these reservoir designtemperature optima depend on the capital costs andsystem performance assumptions used. These pointsare revisited again later in the paper.

Q l m m

' 4b ' 6, ' do ' I & ' IAVERAGE GRADIENT VT(OClkm)-WLOW VT~ZOC/km

LOCUSOF OPTIMUM

The development of the HDR resource at a aparticular location depends largely on being able to x

locating high quality HDR resources is required, the

gain access to high rock temperatures which will leadto acceptable fluid temperatures for generatingelectric power. Although some exploration fordifficulty and costs associated with locating a suitableHDR site are far less than for hydrothermal or fossilfuel resource development. In fact, the more or lessubiquitous nature of the H DR resource suggests thatitsgrade in terms of average geothermal gradient willquality" of a particular site. In Heat Mining,Armstead and Tester (1987) subdivide the grade ofHDR resources in the U.S. into two categories,thermal with above average gradients >38"C/km and

v)" 5 -aI I I I I 1

RESERVOIR PRODUCTION TEMPERATURE (OC)0 50 100 150 200 250 300 350a o -be the single key factor influencing the "commercial-

Figure 2. Generalized effects of resource quality andreservoir performance on busbar generating costs forHDR-produced electricity (from Armstead and Tester(1987)).

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~ . . . . . . . . . . ...... . . . . -- --. . . .... . . .

The temperature changes that may occur in thereservoir output fluid, as well as the rate of powerproduction over the 20 to 40 year lifetime of an HDRsystem, are crucial in determining economic viabilityand in developing an optimal strategy for reservoirmanagement. The most desirable approach is tomaintain a constant output temperature whilemaximizing the mass flow rate of fluid through thereservoir. This will not be possible because anyfinite-sized HDR system will have a finite rate oftemperature decline or drawdown. The energydrawdown rate for a fractured HDR reservoir withlow formation permeability will depend on thefollowing factors:- Accessible fracture surface area and rockvolume-

Reservoir temperature distribution*

Mass flow rate of produced fluid

Distribution of fluid across the factured surface,and through the fractured regionThermal properties of the rock (density, heatcapacity, and conductivity)

*

- Net impedance to flow and allowable pressuredropWater loss rates

High reservoir temperatures, low reservoir flowimpedances, and large reservoir surface areas andvolumes are desirable--leading to lower rates ofthermal drawdown at specified fluid production rates.A key design objective is to maximize the rate offluid throughput and energy production whileminimizing the rate of drawdown. How closely thisis achieved is the primary measure of energyextraction effectiveness. Furthermore with lowimpedance to flow, parasitic pumping losses will beminimized and in the optimal situation, "self-pumpedsystems are possible as a result of buoyancy drive.The issue of water loss raises some speculation aboutinduced seismic effects and the possible economicimpact of a large makeup supply of water in aridregions of the U.S. It should be emphasized thatproper pressure management of the HDR system,which in extreme cases may require downholepumping from the production well, can control oreliminate all water losses should they become acritical issue. Furthermore, in all testing to date, nomeasurable seismic risk has occurred.The production of electricity from HDR geothermalresources can be accomplished in several ways.

Technologies developed for low temperature energysources such as solar, geothermal, and process wasteheat are easily adaptable to the HDR system.Because pressurized hot water ranging intemperatures from about 150 to 300C will beproduced from HDR reservoirs, the followingconversion options are possible (see Kestin, et al.(1980) and Tester (1982) for details):- Single and multi-stage flash cycles

Binary Rankine cycles employing organicworking fluids (ORC)Trilateral Wet Vapor Cycle (TWVC) Smith(1981))Total flow concepts such as the helical screwexpander or the biphase turbine

Because HDR-produced fluids will most likely havelow concentrations of dissolved salts and non-condensible gases, any of the four options cited aboveare technically acceptable in terms of performance --economic factors will eventually determine whatspecific design selections are best suited to aparticular HDR system and its heat rejectionconditions.HDR systems are flexible in their application to avariety of end uses. For example, they could beretrofitted to improve fossil conversion plants usingcogeneration and feed water heating concepts. Insome new design concepts under development,peaking with cyclic energy storage aswell as the moretraditional, base load applications are possible forHDR systems. A key point to remember is that HDRfluid/rock temperatures are selected by choicedepending on end use requirements and the economic"grade" of a specific resource which is largelyexpressed by its average thermal gradient.For all HDR applications that are envisioned, "off-the-shelf,'' commercial power plant systems areavailable. Further development of newer conversiontechnology such as the W C nd total flow systemswill undoubtedly increase the attractiveness of HDRby permitting operation at higher conversionefficiencies.

Comparison of HDR Economic ModelsKey parameters and results for the seven analyzedstudies have been tabulated in Tables 1 and 2 whereone can easily see the extremely wide range ofresource, reservoir, and economic parameters andassumptions. Therefore, any agreement in predictedbreakeven electricity price must be regarded with

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TABLE 1. COMPARISON OF KEY RESOURCE AND POWER PLANT PARAMETERS

(a) Low corresponds to a drawdown parameter 0.0002 kglsqm-s.

TABLE 2. COMPARISON OF NORMALIZED CAPITAL COST COMPONENTS

Notes: (a) Al l costs normalized o 1989$ using cost indexes in Figure 3 for drilling and plant construction costs..Stimulation and exploration cost normal ization based on drilling cost index.Electricity breakeven price normalized on a hybrid, weighted cost index.(b) Total well and stimulation costs are $55.8M or $1117lkWe installed.(c) Based on 90 MWe installed.(d) Conversion rates: $1 per pound for wellfield; $1.6 per pound for power plant.(e) Conversion rate: 1.64 Dm/$. Individual component costs not available in this paper.

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caution. Nonetheless, one can see why certain studiespredict high prices and others lower ones or whereparticular cost components are out of line. We willdiscuss these predictions again la ter in the context ofour generalized HDR economic model.A major purpose of analyzing each study was toextract component cost information in order to guideus in developing a set of composite conditions. Inaddition, by reviewing the other assumptionsregarding reservoir and power plant performanceused in each study, we can construct a reasonablemodel that brackets the range of values assumed.Furthermore, by studying the range of costs and otherfactors, we have developed suitable intervals forparameter sensitivity studies.

HDR Component CostsDrilling and Com pletio n CostsTo establish base case costs and a cost range forHDR drilling, we reviewed all available drilling andcompletion cost data for geothermal (hydrothermaland HDR) and oil and gas wells for the period 1972-1988. The geothermal well costs came from anumber of sources including Carson and Lin (198 l),Entingh (1989), Batchelor (1989a), and Armstead andTester (1987) as well as from the seven case studiesbeing examined. Joint Association Survey (JAS) datafor drilling and completing oil and gas wells in thecontinental U.S. in a particular year were used as areference point to compare actual HDR well costsagainst.In order to normalize well costs to a common yeardollar, a drilling cost index was established as shownin Figure 3. To develop this index, JAS average oiland gas well costs based on total footage for depthsranging from 1250 f t (0.38 km) to 20,000 f t (6.1 knn)were used from 1977 to 1988. In addition, EnergyInformation Administration (EIA) costs for 1976 to1977 (Anderson and Funk (1986)) was used tosupplement the JAS data base. For wells drilledbefore 1976, a 17% annual inflation factor wasassumed.Table 3 gives actual and predicted drilling andcompletion costs for individual wells for HDR andhydrothermal systems. 1988 JAS composite costs forcompleted oil and gas wells are also included inTable 3. Dry well costs were not included in derivingthe JAS composite. Costs for average well depths areshown. Figure 4 presents a composite of actual andpredicted well costs normalized to 1989 $. Thecollection of individual well cost data from a numberof hydrothermal sites in the U.S. compiled by Carsonand Lin (1981) was normalized to 1989 $ and plotted

in Figure 5. The straight line plotted in Figures 4 and5 corresponds to a least squares fit of the 1988 JASoil and gas composite well cost data extrapolated to1989$. One immediately sees that without exception,all hydrothermal and HD R well costs are higher thana typical, average oil and gas well drilled to the samedepth. Furthermore, the bandwidrh of costs for HDRwells lies somewhat above the scatter of hydrothermalwells.Following the methodology described earlier byMilora and Tester (1976) and later updated byArmstead and Tester (1987), we chose to establish arange of expected drilling costs for HDR wells drilledto 10 km depths. In Figure 4, an HDR base casecurve has been plotted with an upper bound (HDRproblem burdened) and a lower bound (HDRcommercially mature) shown.

Stimulation CostsDeveloping and perfecting HDR stimulation methodshave been a major focus of the U.S. and UK R&Dprograms during the past 15 years (see Armstead andTester (1987), Batchelor (1984 (a,b), 1987, 1989b),Tester et al. (1989) and Brown et al. (1990) fordetails). Although the field efforts have madeconsiderable progress, there is not yet sufficientknowledge regarding rock fracturing characteristics toabsolutely guarantee that a fractured network ofsufficient size and viability can be created andconnected to an appropriately designed injection andproduction well system. Given this inherentuncertainty, we must make several assumptionsregarding the formation of such a reservoir system inorder to proceed with an economic assessment ofHDR.All studies have assumed that current fracturingtechnology (or some modest extension of it ) issufficient to create a viable HDR reservoir at depthsof interest. In addition, they have estimated the costsassociated with these stimulation methods. Theseinclude the costs of pumping at high pressures andrates, costs for fluids with special rheologicalproperties, and the costs of diagnostic geophysicaltesting. Figure 6 provides an estimate of stimulationcosts per kWe of net installed capacity for differenttemperature reservoirs. Also plotted on the samefigure are specific estimates for stimulation coststaken from the economic assessment studies. Notethat we have tried to account for the effect ofresource grade on the stimulation costs by plottingcosts as a function of initial reservoir temperature.Lower gradient regions will in general require deeperwells and/or higher well flow rates and thereforeproportionately higher stimulation costs will result.

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500

400

300Xa,2

200

100

0

I > I / I ' I

Basis: 100 in 1965--.t I I

MIT Composite Drilling IndexBasis: 100 in 1976A-..---

s'.'1965 1970 1975 1980 1985 1990Year

Figure 3. Estimated plant construction and drillingcost inflation indexes.

Proiected HDR Well Costs

0.050 2,000 4,000 6,000 8,000 10,000Depth in MetersJAS Composite HDR ActualGeysers Actual Other Hydrothermahydrothermalctual Predicte

Figure 4. Projected HDR well drilling andcompletion costs for the base case with limits forproblem-burdened, commercially mature, andadvanced drilling technology shown.

0.05t ' ' ' ' ' 12,000 4,000 6,000 8,000 10,000Depth in MetersBate, NM Northern NevadaAS Composite Direct Use

Cove Fort UT RooseveltHot Springs ' ~ ~ ~ ~ ' ~ & Ather Wells

Figure 5. Actual hydrothermal completed well costsas a function of depth (adapted from Carson and Lin(1981), Batchelor (1989a), and the Joint AssociationSurvey (1988)).1,000

Bechtel LANL Meridian-,L 2-stage FlashQa, 800

I Conversion rate: 1.6dollars per pound for UK 1

Figure 6. Estimated HDR reservoir stimulationscosts in $ per kWe installed as a function of averageinitial reservoir temperature. Note that ORC(Organic Rankine Cycle) and 2-stage flash refer topower plant choices.-40-


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TABLE 3. ACTUAL AND PREDICTED DRILLING AND COMPLETION COSTS (1989$)

2 GT-23 E-14 E-25 E-3

8 RH-11 (high)10 RH-1 2 (high)9 RH-lZ(lOw)11 RH-1 5 (low)12 IRH-15 (high)iUK (Shock. 1987)- ~- - . .IBechtel(l988)

~ jjapan (1986)Meridian (1987) IMeridian (1987) II

IM-GEO IV-FLIM-GEO IV-BIIM-GEO BR-FLIM-GEO BR-BIIM-GEO CS-FLIM-GEO CS-BIIM-GEO W-FLIM-GEO W-BIIM-GEO GY-DS

JASJASJASPlot # s refer to FigurE

7322,9323,0644,6604,2504,5722,1752,1752,1432,1432,6522,6526,0003,6573,0003,0003,0003,0003,0001,8003,0481,6001,8292,7432,4389143,0489141,5241523,0489541,3401,8592,6283,3764,1084,8345,539

MI0 0601.900 19742.300 19757.300 198011.500 19815.160 19881.240 19811.984 19811.240 19811.984 1981-:.250 19856.0006.9003.800 1 1984

0.956 19861.217 19860.556 19862.032 19860.576 19860.906 1986

T

cost1989M$ . conimc#rts0.187 Fenton Hill Site, New Mexico. USA4.315 Actual costs.4.4656.8278.5455.364 10.921 \RosemanowesSite, Cornwall. UK.1.474 Actual costs.0.921 Conversion rates:1.4742.192 low E $1 per pound.high = $1.6 per pound.3.507 as-recommended by A.S. Batchelor (1989a).8.206 From Camborne School of Mines ($1 per pound).4.006 Predictions or Roosevelt Hot Springs, UT Site.

5.845 /Predicted costs.6.838 I Predicted costs based on Heat Mining.3.7662.973

0.9751.2420.5672.0730.5880.9240.4141.1780.1480.1660.2730.5491.1551.7483.1384.403

M$ = Millionsof US Dollars.Key:IV-FL - mperial Valley Flash, Salton Sea, CA field.IV-BI - mperial Valley Binary, Heber. CA field.BR-FL - Basin and Range Flash, Dixie Valley, NV field.BR-BI - Basin and Range Binary, generic NV field.CS-FL - Cascades Flash, Newberry,OR field.CS-BI - Cascades Binary, generic OR , WA field.W-FL - Young Volcanics Flash, Coso, CA field.W-BI - Young Volcanics Binary, Mammoth, CA field.GY-DS - Dry Steam, The Geysers, CA field (Costs from B.J. Livesay).

from their iM-GEO data base (Entingh, 1989).Only base well costs shown.See key below for hole details.

Actual costs for oi l and gas wellsfrom Joint Association Survey (1988).

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This methodology for estimating stimulation costsfalls short of providing a clear dependence of costson the size of the reservoir. Consequently, weexamined an alternate approach. In Figure 7, theestimated cost of stimulating an HDR doublet systemis plotted as a function total effective reservoirsurface area in mz per kWe of installed capacity. Byusing the installed capacity to normalize costs andreservoir size, the effect of fluid temperature (andhence gradient and depth) on conversion efficiency isaccounted for. Dat a from the Bechtel, LANL,Meridian, Japan, and UK HDR economic studies areplotted along a regressed line (solid) for thecomposite base case that will be used in theeconomic projections described later in this paper.One should note that the Meridian Organic RankineCycle (ORC) estimates were not used in theregression. The dotted lines shown represent twice(200%) and one half (50%) of the composite basecase stimulation costs to illustrate the range ofestimates.Following the Shock (1987) study, Mortimer andMinett (1990) reexamined drilling and stimulationcosts for HDR development in the UK. For adoublet well system 6 km in depth, they estimate adrilling and completion cost of 10.314million f $16.5million per doublet or $8.25 million per well at$1.6/f). This is essentially identical to thenormalized Shock (1987) estimate of $8.42 millionper well. Nonetheless, Mortimer and Minettsstimulation costs are considerably different than thosefrom the Shock study and from this studysprojections. For the same 6 km doublet, Mortimerand Minett estimate a cost for 3 stimulations of 6.004million f ($9.6 million) or 37% of the totalsubsurface system costs. Shock used 10% of the wellcosts ($1.68 million for a 6 km doublet system), whichlies on our base case line in Figure 7. We feel thatMortimer and Minetts approach of using largevolumes of expensive rheologic gelled fluids is toopessimistic based primarily on U.S. fracturingexperience at Fenton Hill. However, in all cases,stimulation cost estimates should be regarded as onlyapproximate in that the technology for creatingcommercial-sized HDR reservoirs is still underdevelopment. In the discussion that follows, weperform sensitivity analyses to further quantify theuncertainties associated with the stimulation costcomponent.

Power Plant CostsEstimated costs in 1989 $/kWe installed for HDRpower plants are shown in Figure 8 as a function ofthe fluid production temperature that would enter theplant. An upper limit of 300C was chosen to avoidproblems of mineral transport and deposition with

the HDR reservoir/power plant system. A nominal50 MWe sized plant has been selected with costsshown for an appropriate range of conditions thatwould be expected for applications in the U.S. Amedian or base case line is shown, but somevariations are anticipated for different sites, geologicand ambient conditions, and plant designs. Forexample, heat rejection using wet cooling with oceanor river water would result in more efficient cycles, ingeneral, with lower costs. Dry cooling or wet/drycooling in regions of high ambient temperatureand/or limited water availability would have lowerefficiencies and somewhat higher costs on a $/kWebasis.In order to achieve a common 1989 $ cost basis forplant costs, a composite cost index was developed.The data used to develop the composite came fromseveral sources including the Chemical Engineering(CE) Plant Cost Index, Marshall and Swift (M & S)Equipment Cost Index, Nelson Refinery ConstructionCost Index, and the Engineering News-Record (ENR)General Construction Cost Index. All cost indexeswere normalized to 100in 1965 and a linear averagefor each year was used to estimate the MITcomposite index as shown inFigure 3. The composite,index was then used to convert all plant costs fromthe studies to a 1989 $ basis. The generic cost curvefromHeatMining (Armstead and Tester (1987)) wasalso normalized and plotted for reference, and as canbe seen from Figure 8, the base case line selected isapproximately the same as the generic example fromHeat Mining at a condensing temperature of 37C. Itis important to emphasize that these cost estimatesare only to be used for HDR resources in thetemperature range shown from 125 to 300C;extrapolation outside the range could lead to seriouserrors.Also plotted in Figure 8 are estimated power plantinstalled costs for the specific designs selected in theHDR economic studies. No total flow plant costs,other than the TWVC, were provided in the sevenstudies. Based on the observed agreement amongU.S. cost estimates, we would anticipate thatestimated installed HDR power plant costs would beaccurate to ?20%. At any rate, the uncertainty inplant costs is significantly lower than for HDR drillingand stimulation costs as discussed above.Generalized HDR Economic Model for EIectricityProductionBuilding on the models and correlations presentedabove, a generalized HDR economic model wasdeveloped. To distinguish it from other treatments,we have labelled it the iMIT HDK economic model,with no Institute endorsement implied. For a given

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n

Japan2-stage Flash UKif 1,000a,Q 0 A

_ -Conversion rate: 1.6 ollars per pound for UK1 1 1 1 1 1 1 ,0 800 1,000

Figure 7. Estimated HDR reservoir stimulation costsin $ per kWe installed as a function of normalizedeffective reservoir surface a rea in m2/kWe installed.

0 200 400 600Effective Reservoir Area (sqm/kWe)

medium O K 2-stage Flash

IANL Meridian JepanO K 2-stage Flash

\ *

d100 150 m 250 300 3 3Geofluid Temperature (deg C)

Figure 8. Estimated HDR power plant constructioncosts for the U.S. Base case cost estimates shouldonly be used in the geofluid temperature range from100" to 300OC.-43-

1 o

0.9

n7 0.8eQs 0 . 7Q\h

0.6

0.5

0.4

I0 5 10 15 20t (Years)Figure 9. Net thermal performance levels as a resultof thermal drawdown and redrilling/restimulation.For comparison, the cases of no drawdown anddrawdown with no redrilling are shown. P(t) is thethermal power extracted at time t.

200I Today'sTechnology 1 I

100Commercial Base Case

52 50(T:a,0a,0v.-2 20>00a,ca , 5>a,Ycua,

e.-.-k loiz

1989$ BasisI I I I

20 40 radient (deg0 C/km)0 100Figure 10. M IT HDR economic model results forelectricity breakeven price as a function of gradientand reservoir performance.


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set of parameters which define a technology case, themodel calculates breakeven electricity price as afunction of gradient. To cover a range of reservoirperformances and costs, the following four technologycases were considered:Today's Technology (TODAY) Case - Reflectstoday's relatively high drilling and completioncosts (see Figure 4, HDR Base Case line) withpoor reservoir performance at a levelcomparable to the Fenton Hill System.Commercial Base (BASE) Case - Keeps samedrilling and completion costs as the TODAYcase, but reflects the improved reservoirperformance required for commercial operation.- Technically Optimized Doublet (DOUBLET)Case - Combines good reservoir performancewith optimized drilling and completion costs(see Figure 4, HDR Commercially Mature line).Technically Optimized Triplet (TRIPLET) Case- Maintains same optimized drilling costs asDOUBLET case, but improves reservoirperformance with a configuration of 1 injectorand 2 producer wells per reservoir.

The specific parameters used to define these casesare detailed in Table 4. A thermal drawdown rate of2% per year was selected. This corresponds to aneffective drawdown parameter of 1.4 x lo4 kg/m*.s.Redrilling and restimulation are done at 5 yearintervals to restore reservoir temperatures to theirinitial values. This is shown in Figure 9. Forcomparison, the cases of no drawdown and drawdownwith no redrilling are also shown. For the cases withdrawdown, the thermal power levels (P(t)) follow anerror function dependence on effective eservoir heatas given by equation A-5 in Appendix A.transfer area (< A> ), mass flow rate ( d and time (t)

The model calculates several important engineeringparameters, including average well depth, initialreservoir temperature, geothermal fluid temperatureand availability, utilization efficiencies, effectivereservoir area, overall pressure drop, and pumpingpower. Costs are calculated on a per kWe installedbasis. This has the advantage of eliminating plantsize as a model parameter. However, results will bemost accurate for facilities in the 25-100 MWeinstalled capacity range, since this is the range uponwhich most of the correlations are based. Thiscapacity range also corresponds to the most probablesize of HDR plants to be built.A fixed annual charge rate approach is employedbecause it is easy to implement and use. A 15.34%annual charge rate suggested by Entingh (1987) was

incorporated. In our preliminary analyses, we usedboth a fixed annual charge rate and a levelizedlifecycle approach. Our results showed both methodsgive the same trends, with the fixed charge rateyielding about 15% higher electricity breakevenprices.The drilling and completion, stimulation, and powerplant costs used were estimated from the base casesshown in Figures 4, 7, and 8, respectively. Redrillingand rest imulation costs were averaged over the plantlifetime and treated as increments to the operatingand maintenance (O&M) costs. All results arepresented in 1989 dollars.The key results of theMITHDR economic model aregiven in Figure 10 and Table 5. To get a better feelfor the results, compare Figure 10 o Figure 2. (Note:Figure 10 has a logarithmic y-axis compared to alinear one for Figure 2) . These graphs have the sameform and the discussion of Figure 2 applies equally toFigure 10. As the gradient decreases, drilling andcompletion costs become more dominant and drivethe busbar costs up exponentially. Much of the rangein costs at a given gradient is a result of reservoirPerformance. Poor performance translates into lowgeothermal fluid flowrates per well pair or perreservoir, which drive up the costs.One place the model results differ with the earlierdiscussion is in optimum reservoir productiontemperatures (i.e., drilling depths). Figure 11summarizes the model results. At 40"C/km andabove, the model suggests drilling to a depthassociated with the maximum allowable reservoirtemperature (300C). Only at lower gradients is anoptimumfound at lower reservoir temperatures (180-200C for 20"C/km and 220-245C for 30"C/km).The optimization of other reservoir and power plantdesign parameters are discussed in a generaltreatment outlined in Appendix A.Figure 12 compares the MIT HDR economic modelresults to the breakeven electricity price reported inthe seven other economic studies analyzed.Predictions using the generalized HDR economicmodel are in general agreement with the normalizedresults of the several of seven previous HDReconomic studies. This agreement, however, isfortuitous unless the individual component costs foreach model are close to one another. Most of thetime, there was only minimal agreement on specificcomponent costs such as drilling, stimulation, orpower plant costs. But to the extent that the studiesagreed on their methodology, we were able to usetheir data to justify and specify critical costcomponents in the revised composite model.On another note, we recently received a copy of a

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TABLE 4. MIT HDR ECONOMIC MODEL- CASE DEFINITIONS

708090

TABLE5. MIT HDR ECONOMIC MODEL - RESULTS

5.8 1 1 !33: 1.17.1 5.36.6 4.9 3.8 3.5

40 18.450 12.1 8.260 9 4 6 6 4.1

100 6.2 4.7 1 3.7 3.4

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Model Predictions forOptimum Well Depth

2

I I I40 60 804

I I 1 I I40 60 80 100 120

Figure 11. MIT HDR economic model predictions foroptimum well depth as a function of gradient for thecommercial base case.

20 Geothermal Gradient (de g C/km)

I100I I I40 60 80 100 12020 Gradient (de g C/km)Figure 12. Comparison of M IT HDR economic modelresults to those of the seven economic studiesanalyzed for this report. UK estimates based onHarrison, et al. (1989) study (UK-CSM point) addeda t a conversion rate of $1.6/f.

200

I I I I60 80 10020 40Geothermal Gradient (deg C/km)Figure 13. Sensitivity of the M IT HDR economicmodel base case to doubling and halving the drillingand completion costs.

h Stimulation Cost Sensitivity1989 $ Basis

cn

c, 1 \%a, 10Ym

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paper by Harrison, Doherty, and Coulson (1989) thatupdates the Shock (1986) study of HDR economics inthe UK . The resource and reservoir performanceparameters used by Harrison, et al. are identical tothose used by Shock (see Table 1). However,Harrison and coworkers based their power plantdesign on a two stage-flash system. Although theirresults are not presented in detail, their estimate of8 pence/kWh (approximately 12.8q/kWh) is veryconsistent with our projections for mid- to low-gradeHDR resources.Case studies were performed with the MIT HDReconomic model to determine breakeven pricesensitivity to certain model parameters. This phaseof the investigation focused on areas with the greatestuncertainty, specifically optimum well depth, drillingand completion costs, stimulation costs, and thermaldrawdown rates. Our analysis showed:

The price versus depth curve is fairly flat nearthe opt imum (see Figure 2). This leads to someuncertainty as to optimum drilling depth.Price is highly sensitive to drilling andcompletion costs (see Figure 13). At low andmid-gradients, this sensitivity is an order ofmagnitude greater than sensitivity to stimulationcosts (see Figure 14) or to power plant costs.The MIT HDR model is much more sensitive toproduction flowrate than drawdown. Thus,maximizing flowrate per reservoir is desirable,even at the expense of increasing drawdownrate. Of course, this principle cannot be carriedto the extreme limit of an unacceptably highrate of drawdown that would not permit anadequate payback of the capital investment indrilling and stimulation.

Conclusions and RecommendationsSeveral general conclusions can be drawn from ourprojections assuming base case performance andcosts:1. High-grade (80C/km) HDR resources arecompetitive at today's energy prices.2. Mid-grade (50C/km) HDR resources are only

marginally competitive at today's energy prices.With higher oil prices >30$/bbl and/orenvironmental costs associated with fossil-fuelfixed systems, e.g., an acid rain or carbon tax,mid-grade HDR systems would be competitive.3. Low-grade (3OoC/km) HDR resources would notbe competitive for electricity production untilsignificantly higher energy prices exist. Although

it should be noted for direct heat (space orprocess heating) or for cogeneration applications,low-grade HDR resources would compete muchmore favorably because Second Law efficienciesfor conversion of HDR thermal energy intoelectricity are not relevant.As a result of our economic analysis, we can identifywhere research should be focussed to improve thecommercial viability of lower grade HDR resources(


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especially helpful. A. Carbone and J. Hale aregratefully acknowledged for their help in preparingthe paper. The Geothermal Technology Divisionof the U.S. Department of Energy, Sandia NationalLaboratories and the MIT Energy Laboratoryprovided partial support for this project. The viewsand opinions presented in this report are solelythose of the authors, and as such, do not necessarilyreflect an endorsement by the U.S. Government, itsagencies or laboratories.

ReferencesAnderson, T.C. and V.T. Funk, "Indexes andEstimates of Domestic Well Drilling Costs 1984and 1985," Energy Information Administrationreport DOE/EIA-0347 (84-85), Washington, D.C.(1986).Armstead, H.C.H. and J.W. Tester, Heat Mining,E.F. Spon, London (1987).Batchelor, A.S., "Hot Dry Rock GeothermalExploitation in the United Kingdom," ModernGeology, Vol. 9, pp, 1-41 (1984a).Batchelor, A.S., "Hot Dry Rock ReservoirStimulation in the UK--an Extended Summary," and"An Overview of Hot Dry Rock Technology," 4thInt. Conf. on Energy Options, The State ofAlternatives in the World Energy Scene, IEEPapers, London (1984b).Batchelor, A.S., "Development of Hot Dry RockSystems in the UK," IEE Proc. A, Vol. 134(5),London pp. 371-380 (1987).Batchelor, A.S., Geoscience Ltd., Falmouth, UK,personal communication, 12 Dec 1989 (1989a).Batchelor, A.S., "Hot Dry Rock and ItsRelationship to Existing Geothermal Systems,"Camborne School of Mines International Hot DryRock Conference, Camborne, Cornwall, England,July 1989, 20 pages (1989b).Bechtel National, Inc., "Hot Dry Rock VentureRisks Investigation," Final report for the USDOE,under contract DE-AC03-86SF16385, SanFrancisco, CA (1988).Brown, D.W., R.M. Potter, and C.W. Myers, "HotDry Rock Geothermal Energy - An EmergingEnergy Resource with Large Worldwide Potential,"in Energy and Environment in the 21st Century,MITPress, Cambridge, MA (1990).Carson, C.C. and Y.T. Lin, "Geothermal Well Costs

and Their Sensitivity to Changes in Drilling andCompletion Operations," Proc. Int. Conf. onGeothermal Drilling and Completive Technology,Albuquerque, NM, pp. 21-23 (1981).Cummings, R.G. and G.E. Morris, "EconomicModeling of Electricity Production from Hot DryRock Geothermal Reservoirs: Methodology andAnalyses," EPRI report EPRI EA-630, Palo Alto,CA (1979).Entingh, D., "Historical and Future Cost ofElectricity from Hydrothermal Binary and Hot DryRock Reservoirs, 1975-2000," Meridian Corp. report240-GG, Alexandria, VA (1987).Entingh, D., personal communication, MeridianCorp., Alexandria, VA (1990).Harrison, R., P. Doherty, and I. Coulson, "Hot DryRock Cost Modeling," Camborne School of MinesInternational Hot Dry Rock Conference, Camborne,Cornwall, England (1989).Hori, Y. et al., "On Economics of Hot Dry RockGeothermal Power Station"and related documents.Corporate Foundation Central Research Institutefor Electric Power, Hot Dry Rock GeothermalPower Station Cost Study Committee report 385001,Japan (1986).Joint Association Survey on Drilling Costs for Years1978-1988, American Petroleum Institute,Washington, D.C. (1978-1988).Kestin, J. et al. (eds.), A Sourcebook on theProduction of Electricity from Geothermal Energy,U.S. Government Printing Office, Washington, D.C.(1980).Milora, S.L. and J.W. Tester, Geothemal Energy asa Source of Electric Power, MIT Press, Cambridge,MA (1976).Mortimer, N.D. and S.T. Minett, "Hot Dry RockGeothermal Energy Cost Modeling: Drilling andStimulation Results," in R. Baria (ed), Hot D I ~ockGeothermal Energy. Proceedings of the InternationalHo t Dry Rock Geothermal Energy Conference,Camborne School of Mines, 27-30 June 1989,Robertson Scientific Publications, London (1990).Murphy, H.D., R. Drake, J.W. Tester, and G.A.Zyvoloski, "Economics of a 75-MWe Hot Dry RockGeothermal Power Station Based upon the Designof the Phase I1 Reservoir at Fenton Hill," LosAlamos National Laboratory report LA-9241-MS,Los Alamos, NM (1982).

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Shock, R.A.W., "A n Economic Assessment of HotDry Rocks as an Energy Source for the U.K.,"Energy Technology Support Unit report ETSU-R-34, UKDOE, Oxfordshire, UK (1986).Smith, I., "The Trilateral Wet Vapor Cycle," CityCollege of London, London, UK, working paper(1981).Smolka, K. and 0.Kappelmeyer, "Economic CostEvaluation of HDR Power Plants" Hot Dry RockGe othe ima l Energy. Proceedings of the InternationalHot Dry Rock Geothermal Energy Conference,Cam bome School of Mines, 27-30 June 1989, R.Baria (ed.), Robertson Scientific Publications,London (1990).Tester, J.W., "Energy Conversion and EconomicIssues for Geothermal Energy," Handbook ofGeothermal Energy, Ch. 10, L.M. Edwards et al.(Eds.), Gulf Publishing, Houston, TX (1982).Tester, J.W., D.W. Brown, and R.M. Potter, "HotDry Rock Geothermal Energy--a New EnergyAgenda for the 21st Century," Los AlamosNational Laboratory report LA-115 14-MS,LosAlamos, NM (1989).Tester, J.W. and H.J. Herzog, "EconomicPredictions for Heat Mining: A Review andAnalysis of Hot Dry Rock (HDR) GeothermalEnergy Technology," MIT Energy Laboratoryreport MIT-EL 90-001, Cambridge, MA (1990).

Appendix A A general approach to optimizing theperformance of an HDR systemIn our earlier work (Milora and Tester, 1976; Tester,1982; and Armstead and Tester, 1987) we consideredhow several control variables affected the breakevenprice of HDR-generated electricity. These includedwell depth (Z), gradient (VT), and mass flow rate(G). Given that we have constructed an analyticalrepresentation of system performance in terms ofthermal or electrical power output, it is possible, atleast conceptually within a somewhat idealizedframework, to describe a general multiparameteroptimization to include the effects of all importantinput and control variables relating to resource andreservoir properties. The set of variables includes:

Input Variables1. gradient (VT)2. initial flow impedance (I(t=O))3. rock and water thermophysical properties4. ambient heat rejection condition (To)( P C PA.

Control VariablesI. mass flow rate (&)2. reservoir size ( < A > )3. reinjection temperature (Tinj)4. well depth (Z)5. redrilling strategyThe total electrical output over the production periodof the reservoir (tJ can be expressed in integral formas:

s ( 5 ) - /d' [nitly(~l))AB(~r),T~To)A-1)- P-(niAldtwhereni = mass flow rateV U = utilization efficiencyAB = availability = AH-TOASover {T(t),Tinj}Ppump= pumping power required to circulatefluid at a flow rate & and time tT(t) = reservoir fluid production temperatureTinj = reinjection temperatureTo = ambient temperatureIn turn, we can express the dependence of 0 on T(t)as:

to empirically match plant performance data where aand p are adjustable parameters (see Armstead andTester (1987), Figure 14.3, p. 404). The availability,in turn, can be calculated rigorously as:

where C,(T) is the heat capacity of saturated liquidwater which can be approximated as:C,,(7) - A + B T + C l 2 ('4-4)

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where A,B, Care empirically fit parameters to steamtable data. The outlet reservoir fluid productiontemperature T(t) will, in general, be a complexfunction of the geometry, flow characteristics, andthermophysical properties of the reservoir. Forconceptual sensitivity studies, a simple idealizedmodel involving 1-Dflow and 1-D rock conductioncan be used. (Armstead and Tester (1987) andTester and Herzog (1990)). In this case,

whereTrm = initial rock temperature at depth'r = rock thermal conductivityQr = rock thermal diffusivityCp = fluid heat capacity< A > = effective reservoir heat transfer areaRedrilling and restimulating the reservoir at sometime t < t, will, of course, change the formulation ofequation (A-5).

The pumping power required to circulate fluidthrough the reservoir/well bore system is dependenton frictional losses in the well bore and reservoir.Bouyancy effects caused primarily by the densitydifference between the hot and cold legs of thesystem reduce the pumping requirement. Reservoirlosses are typically expressed in the form of animpedance term (I) which must be specified whilewell bore losses can be estimated for given hole andcasing diameters, well depth, and mass flow rate.For a complete economic analysis, the capital andoperating costs for creating and maintaining the wellbore/reservoir system and for constructing andrunning the power plant must be incorporated.Nonetheless, much can be learned by examining howthe net electrical output over a specified productionperiod depends on the control variables. Electricalpower is the commodity that is priced to meet capitaland operating cost burdens to establish a so-calledbreakeven price.

In our earlier work, we have optimizedperformance with respect to only one controlparameter at a time, such as geothermal gradient andoptimal well depth as shown in Figure 11. Each pointon that figure was established by varying depth at afixed gradient to establish aminimum breakeven costfor electricity. (see Tester and Herzog (1990) fordetails). Above a gradient of 40"C/km the costversus depth curve had a very shallow minimum orwas still decreasing when the maximum 300Creservoir temperature boundary was reached. Thisresult was contingent on specifying all otherparameters, including flow impedance, thermaldrawdown rate, and well flow rate at their base caseconditions as given in Table 4.

With the general model described above, we canbegin to study how performance is affected by otherimportant control variables from a fundamentalperspective. For example, how does drawdown rateexpressed by ni/ influence the net electricaloutput from an HDR system over its lifetime or howdoes reinjection temperature affect output. Equation(A-1) can be modified to more clearly show this effectby setting the parasitic pumping power to zero anddividing by < A >

Figures A-1 and A-2 illustrate the effect ofdrawdown on net output per unit reservoir area for aHDR system at 50"C/km and 5.7 km depth with areinjection temperature of 55Cwith no redrilling orpumping losses. The four graphs corresponding topoints A, B, C and D show the power output versustime over a 20 year production period at particularvalues of ni/. Note that maximum electricaloutput is achieved as the production rate fi isbalanced with reservoir size at a drawdownparameter ni/ of about 0.5 x l o4 kg/m2s.Higher values of ;/ result in too rapidproduction temperature decline reducing perfor-mance by lowering v u and AB while lower valuesresult in suboptimal output controlled by the &/term in equation (A-6).

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Gradient-53 deg Ckm. Depm=5 7 kmb ln ld lonT6mpe~Rlrw=55 eg C, T0=25 deg CAssumes no redrillingand no pumping losses

20 year production period at 100% load factor 0 10 5 1 5 2 2 5 3Drawdown Parameter, m k A > (0mi kgfqm-s)

Figure A-2. Time histories of electrical poweroutput for representative values of drawdownparameters (&/).

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