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Productivity in Exploration Drilling1

by

Petter Osmundsen*, Kristin Helen Roll** and Ragnar Tveterås**

* University of Stavanger / Norwegian School of Economics and Bus. Adm.

** University of Stavanger / International Research Institute of Stavanger

Lead author: Petter Osmundsen, Professor, Department of industrial economics and risk management,

University of Stavanger, NO-4036 Stavanger, Norway. Email: Petter.Osmundsen@uis.no. Tel: +47 51 83

15 68. Mobile: +47 99 62 51 43. Home page:

http://www5.uis.no/kompetansekatalog/visCV.aspx?ID=08643&sprak=BOKMAL

Abstract

Recently, we have seen falling oil prices combined with sticky costs at a high level in the

petroleum industry. This causes project postponements, thus challenging reserve replacement of

oil companies and security of supply for consumers. Costs are particularly high for drilling. High

rig rates are obviously important. In addition we experience a dramatic fall in drilling

productivity. This paper analyses the development in drilling productivity in exploration wells at

the Norwegian continental shelf. A unique dataset allows us to apply econometric analyses to

ascertain vital explanatory factors for variation in drilling productivity over time and between

different wells.

1 We would express our thanks for rewarding conversations with and comments on the article itself from a number of key specialists in oil the Norwegian Petroleum Directorate, oil companies, rig contractors and oil service enterprises.

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1. Introduction

Rig hire and the cost of oil services are the dominant components in drilling expenses, as

illustrated in Figure 1 by a representative well. Drilling expenses have increased sharply in

recent years. Key causes of this increase include declining drilling productivity and higher rig

rates. Oil operations on the NCS - as in other petroleum provinces – have recently been

characterised by a shortage of rigs and very high rig rates. In new contracts for high-spec semi

rigs on the NCS, e.g., the day rate has increased from 147,500 USD per day in July 2004 to

530,820 USD in September 2008, i.e., an increase of 260 per cent over a four-year period, see

Figure 2. This reflects the oil industry boom sparked by the high price of crude, and the fact that

few rigs were built over a fairly lengthy period.

710

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35Administration

Equipment and materials

Logistics

Rig

Services

Figure 1. Typical composition of drilling costs. Percentage shares.

Source: Data from rig contractors on the NCS.

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Figure 2: Development in day rates for high-spec semi rigs operating on the NCS (new contracts), and high-spec jackups in North West Europe, from 2004 to 2008. Note from the diagram that there are not available data for new semi contracts mid 2008. Data source: ODS-Petrodata and North Sea Rig Report.

At the same time, a disturbing decline in drilling efficiency - measured by the industry standard

drilling meters per day - can be observed. For instance, as shown by Figure 3, from 2004 to 2008

exploration drilling slowed down from an average 144 to 67 meters by day - a reduction of more

than 50 per cent. Although there are not studies on drilling efficiency available for many regions,

anecdotical evidence suggests that a decrease in drilling efficiency is a global tendency. For

instance, Society of Petroleum Engineers have scheduled a conference in Spain in September

addressing reduced drilling efficiency; ‘The number of meters drilled per day is falling

dangerously and continuously.’

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Figure 3: Average meters drilled per day. Exploration wells on the NCS, from 1966 to 2008. Annual number of wells in brackets. Black vertical lines indicate standard deviation. Data source: Norwegian Petroleum Directorate.

The combination of falling drilling efficiency and increased rig rates have led to a dramatic

increase in costs. Helge Lund, CEO of the major player at the NCS – the oil company

StatoilHydro - has referred to this as a ‘cost tsunami’. The oil companies could cope with the

dramatic cost increase when the oil price was above 100 USD per barrel, but the present cost

structure is seriously challenging the development of new reserves at the current downturn of the

oil price; see Figure 4.

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Figure 4: Oil prices in 2008, Brent Platts Dated

As a response to dramatic increasing costs - that seem sticky downward - StatoilHydro has

announced cost cuts and postponement of new projects. This is not unique to the NCS. Other oil

companies, e.g., Royal Dutch Shell and CococoPhillips, have announced similar strategies on a

global basis.2

This article analyses one of the major drivers of the cost inflation in the oil industry - the

decrease in drilling efficiency. Our case is exploration drilling on the NCS, where we have

access to a unique panel data set. Relevant explanatory factors for variations in drilling

productivity over time and between fields are examined by means of econometric productivity

analyses, which allow us to account for several covariates simultaneously. The unique dataset

from the NPD - covering all exploration wells on the NCS - allows us to apply econometric

2 Cost cuts in Royal Dutch Shell were reported in April, e.g., before the dramatic fall in the oil price, see TimesOnline, http://business.timesonline.co.uk/tol/business/industry_sectors/natural_resources/article3803252.ece. Falling oil prices will accentuate cost cuts. Recently, ConocoPhillips announced a plan to cut four per cent of the work force; see the report in Houston Cronicle, http://www.chron.com/disp/story.mpl/hofhfhfhtstories/6215681.html

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analyses to ascertain the vital explanatory factors for variation in drilling productivity over time

and between different wells.

As dependent variable in the econometric analysis we use meter per day, which is the

industry standard for measuring drilling efficiency. We do want to emphasize that other

measures than drilling speed are necessary to identify value creation in drilling. In addition to

drilling speed, which affects the cost side, the amount of oil and gas which can be produced must

certainly be taken into account. It is not only a question of drilling fast, but also of drilling

correctly. A trade-off may need to be made here, at least in parts of the well path. Drilling speed

in exploration must not come at the expense of the primary objective of gathering well

information. When rigs are scarce, however, efficient utilization of rig time becomes particularly

important.

We analyse an extensive panel data set from the Norwegian Petroleum Directorate (NPD),

providing detailed information on all exploration wells drilled on the NCS in the time of

operation, i.e., from 1966 to 2008. The article also draws on a number of meetings, presentations

and conversations with key specialists in the NPD, oil companies, rig contractors and oil service

enterprises.

2. Existing literature

In this section we discuss relevant studies and position our paper in the literature. It seems like

the literature analyzing recent developments in exploration drilling productivity and underlying

causes is fairly limited, particularly studies published in peer-reviewed journals.

Parts of the dataset on exploration drilling on the NCS that is employed in this paper has

been analysed previously, to ascertain the determinants of variations in the overall exploration

level and reserve generation. Mohn and Osmundsen (2008) specify and estimate an econometric

model of exploration and appraisal drilling for the NCS, having the number of wells as the

dependent variable, and with explanatory variables including the oil price, cumulative

discoveries and open exploration acreage. In Mohn (2008) the dataset is used to analyse the

generation of oil and gas reserves from exploration, having drilling efforts, drilling success and

average discovery size as endogenous variables. Petroleum reserve additions per unit of drilling

effort has also been analysed previously on US data by Iledare and Pulsipher (1999) and on

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British data by Kemp and Kasim (2006). Note that our approach is complementary in that we

analyse productivity at a different level. A full economic approach would be to examine the net

present value of exploration activity. To do so one would have to account for the quality and

future value of the volumes of the discovered oil and gas, which of course is hard to achieve. The

common approach, therefore, is to analyse the volumes of oil and gas that are added. We move

even farther up the value chain by analyzing a purely technical drilling efficiency, i.e., the metres

drilled per day, and ascertain how this measure is affected by economic and technical

parameters. Meters drilled per day is the standard key performance parameter in drilling. Our

rationale for choosing this approach is that a dramatic drop in meters per day - combined with

very high rig rates - is currently perceived by oil companies as one of the main challenges to

exploration drilling on the NCS. The maturity of the Norwegian shelf and small discovery sizes

is certainly challenging. However, success rates of drilling have been very high on the NCS over

the last years, and although discovery sizes are considerably down many of the discoveries are

economic to develop by tying in to existing infrastructure. Surging drilling costs, however, are

now seriously challenging the economics of exploration drilling. Accordingly, there is much

focus in the industry as to the underlying factors that determine drilling speed.

Kellogg (2007) empirically examines the importance of relationship-specific learning using

high-frequency data from onshore oil and gas drilling in Texas. He uses the time necessary to

drill a well as the measure of drilling productivity, accounting for the depth of the well being

drilled. He argues that the measure of drilling speed parallels the way producers and engineers

actually view drilling efficiency. The analyses show that the joint productivity of a lead firm and

its drilling contractor is enhanced significantly as they accumulate experience working together.

Snead (2005) analyses the increased role of deep drilling in Oklahoma. His descriptive

analysis of the relationship between well costs and well depth suggests that there is an

exponential increase in average well costs per feet as depth increases. In addition to a decline in

productivity this also implies that exploration becomes more capital intensive as well depth

increases. On the other hand, there is a potential payoff from deep drilling as the average gas

production from deep wells has been much higher than for shallow wells.

As drilling operations usually are subject to outsourcing, there is a strand of literature on

drilling and oil service contracts that shed light on drilling efficiency. Corts (2000) describes the

trade-off between turnkey and day-rate contracts. Turnkey contracts give the rig contractor

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stronger cost incentives and can cut drilling time and costs. The limited utilisation of turnkey

contracts for drilling is attributed by Corts in part to the multi-task problem - rewarding one

measurable dimension (metres drilled per day) can be at the expense of other important and hard-

to-measure quality indicators such as efficient reservoir drainage and information gathering.

Corts and Singh (2004) show that repeat contracts between an oil company and a drilling

contractor led increasingly to the abandonment of the turnkey model in favour of day rates. They

explain this by the build-up of relationships and trust, which reduces the incentive problems and

thereby the need for high incentive intensity. Osmundsen et al. (2008) describe and analyse

incentives for drilling contractors on the NCS. These are directly represented by the

compensation formats utilised in the present and in the consecutive drilling contracts, which are

analysed. The paper also analyses incentives are indirectly provided by the evaluation criteria

that oil companies use for awarding drilling assignments. An analogous study for oil service

contractors on the NCS is provided in Osmundsen et al. (2009). For a discussion of the

relationship between health, safety and the environment (HSE) and incentive systems in drilling,

see Osmundsen et al (2006).

3. Empirical specification and data

We estimate an econometric model of drilling productivity on a log-log form for the continuous

variables, which simplifies derivation of elasticities. The model is flexible in the sense that

continuous variables are specified as second-order and interacted variables, and will therefore

allow for a complete specification of substitution patterns among continues variables, i.e. we

have a translog type model. The model is on a general form specified as

(1) lnY = ΣiαilnXi + 0.5ΣiΣjαijlnXilnXj

+ αtt + αt2t2 + αt3t3 + ΣiαitlnXi·t

+ αOPlnOilPrice + αOP2lnOilPrice2

+ αWNCSlnWellsNCS + αWNSC2lnWellsNCS2

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+ ΣrμrOilCoTyper + ΣsμsFacilityTypes + ΣeμeFacilityExperiencee

+ μDDiscovery + μWPurpose +μWBSWellboreStatusPA + ΣaμaAreaa,

where the dependent productivity variable, Y, is as previously mentioned, drilled meters per day

and represents drilling efficiency. X is continuous variables in the model, including well depth in

meters, water depth in meters, and well temperature. The terms with the time-trend variable t are

included to control for technological change. The “WellsNCS” variable is the total number of

exploration wells drilled in current year on the NCS, and is together with “OilPrice” (which is

lagged one year) a proxy for the supply and demand conditions in the drilling market. An

“OilCoType” dummy describes the type of oil company, with “mid caps am” and “three sisters”

as reference category, “rest” as dummy variable 1 and “mid caps euro” as dummy variable 2.

The “FacilityExperience” dummy variables control for drilling experience among drilling

facilities, where the facilities are separated in three groups based on their drilling experience on

NCS.3 A “FacilityType” dummy variable is included to control for the type of drilling rig. The

most common facility type is semisub steel, and this is the default category. Several

characteristics of the well are included in the model as dummy variables. The

“WellboreStatusPA” variable controls for the wellbore status of the well. Most of the well’s

status is plugged and abandoned (P&A). We also control for discovery status of the well through

the “Discovery” dummy variable, and the area where the well has been drilled by the “Area”

dummy variables. The wells are drilled in the three major offshore regions on the Norwegian

Continental Shelf – the North Sea, the Norwegian Sea and the Barents Sea. Finally, we control

for the “Purpose” the drilling of specifying if the well is a wildcat or appraisal.

3 If the drilling facility has drilled less than 10 times at NCS over the data period it is calcified as less experienced,

between 10-30 times intermediate experienced, and above 30 times much experienced.

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Our data set is retrieved from the data bases of the Norwegian Petroleum Directorate, who

has collected and processed information and statistics on Norwegian oil and gas activities since

the early 1970s. We have time series for all variables over the period 1965-2008, split between

the three major offshore regions on the Norwegian Continental Shelf – the North Sea, the

Norwegian Sea and the Barents Sea. The long time span of our data allows us to account for

several oil price cycles, as well as technological development. Summary statistics of the

estimating sample is provided in table 1. Given the challenging nature of large-scale offshore oil

and gas operations on the NCS, the dataset comprises the vital companies in the oil business. The

companies participating as operators on the NCS include all super majors, and major oil service

companies like Halliburton, Baker Hughes and Schlumberger are present in Norway. For details

on NCS resources and participants, see Norwegian Petroleum Directorate (2007) and Ministry of

Petroleum and Energy (2008).

4. Empirical results

The production function is estimated using OLS.4 Before estimation, a classical additive

disturbance term is appended to the model. Estimated coefficients with heteroskedasticity-

consistent standard errors and associated t- and p-values are presented in Tables 2, and derived

elasticity estimates from the model is presented in Table 3. Our empirical findings correspond

largely with the a priori expectations that we have made from conversations with industry

specialists. According to the estimated model the contribution from different factors in

explaining drilling efficiency are as follows: (1) Purpose: Wildcats are more productive than

appraisal wells. (2) Area: The Barents Sea wells are the least productive, Norwegian Sea wells

are most productive, and North Sea wells are in between. (3) Discovery status: Wells with

discovery are less productive than dry wells. (4) Drilling depth: Deeper wells are less productive

than shallow wells, although the negative effect of increasing drilling depth is not statistically

significant. (5) Water depth: Water depth has a negative effect on productivity. (6) Oil price:

4 Stata (10) software was used for estimation.

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Drilling productivity slows down when oil prices increase. (7) Total number of exploration wells

drilled on NCS: Drilling productivity is lower in years when many wells are drilled on the NCS,

although the effect is not statistically significant. Part of the explanation for this is that a high

activity level is associated with a high oil price. (8) Technological change over time: We can

trace a positive effect on productivity that we can attribute primarily to technological progress.

(9) Well temperature: There is no statistically significant effect from well temperature on drilling

productivity.

Our findings can be explained as follows. Drilling speed is lower in appraisal wells than for

wildcats, due to more testing time. Our time measure includes the testing time. We find that

drilling is slower in the Barents Sea, even if we account for differences in see deep, water depth,

etc. Possible explanation to this is tougher climate conditions and larger logistic challenges due

to longer distances from supply clusters. The lower number of wells in the north also means that

this region has travelled a shorter distance down the learning curve. The oil industry also faces

tougher environmental standards in the north, negatively affecting drilling speed. Wells with

discovery are slower to drill due to time spent on testing. High water depth slows down drilling

speed. This is not surprising, as our drilling metre measure starts at the sea bed. Thus, a high

water depth takes time for the drilling company without contribution to the key performance

indicator - metres per day. High oil prices are associated with a strong business cycle for the oil

industry. High activity levels imply scarcity of rigs. Thus, less adequate rigs are being used at the

margin, reducing average productivity. Moreover, at a high business cycle for the oil industry

there are more likely to scarcity of personnel and bottle necks at other crucial supply services in

drilling, thus driving up the non-productive time. The same reasoning applies to our finding that

productivity is lower at years of a high number of exploration wells. Finally, over time there

have been several technological changes that have contributed positively to drilling speed, e.g.,

the introductions of the top drive and measurement while drilling. Industry specialists are not

surprised of our finding that temperature is insignificant for drilling speed, as temperatures are

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not especially high on the NCS. Moreover, high temperatures impose special requirements on

equipment, thus affecting costs more than drilling speed.

5. Conclusions

A dramatic increase in drilling costs is a particular challenge in the current situation when much

effort is put into increased oil recovery from mature fields and development of new deep-water

fields, as both these project types are drilling intensive. The increase in drilling costs represents a

challenge both to international oil companies (IOCs) and oil consumers. The IOCs are already

struggling to maintain production and reserves, and sticky costs at a high level combined with a

decrease in oil price make this much harder. Even for projects that are still profitable, the

decrease in drilling efficiency is problematic as it reinforces the problem posed by scarcity of

rigs.

In World Energy Outlook 2008, IEA undertakes a field-by-field analysis of production

trends at 800 of the world’s largest oilfields. Through a bottom-up analysis of upstream costs and

investment, they make an assessment of the potential for finding and developing new reserves.

They conclude that the immediate risk to supply is not one of lack of global resources (estimated

remaining proven reserves have almost doubled since 1980), but rather a lack of investment.

Upstream investments have been rising rapidly in nominal terms, but much of the increase is due

to surging costs. In cost-inflation adjusted terms, investments in 2007 were according to IEA 70

per cent higher than in 2000. Worldwide, upstream costs rose on average by an estimated 90 per

cent between 2000 and 2007. Most of the increase occurred in the period 2004-2007. IEA warn

that there remains a real risk that underinvestment will cause an oil-supply crunch.

This article analyses one of the major drivers of the cost inflation in the oil industry - the

decrease in drilling efficiency. The output of our analyses can be useful, in two ways: 1) by

detecting the underlying factors that influence drilling efficiency, we provide a basis for making

predictions of future drilling efficiency levels. For instance, what is the likely effect on drilling

efficiency of the current oil price fall? 2) By indicating the major explanatory factors for

variations in drilling efficiency over time, we can give advice on where the oil industry has the

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largest potential for improvement. - Is it in a careful management of drilling capacity versus

drilling tasks, or is it primarily in better handling of deep-water drilling?

State-of-the-art rig activity monitoring presents an opportunity to revolutionize the way in

which contractors are rewarded for above average performance, technically or in safety terms.

For many years the few contractual incentives available and utilized have concentrated on very

simple metrics, such as rig uptime and on drilling rates. A successful well for most old-time

drillers was one that reached total depth quickly and without significant lost time. Whether the

well ultimately became an effective producer, drilled and completed with minimum damage to

the formation and with high mechanical integrity, was not considered. Rapid drilling is not

always compatible with good reservoir utilisation and efficient information gathering, so a trade-

off must be made here.

The companies and the authorities basically share a common interest in the best possible

utilisation of scarce drilling capacity. Losing resources which could otherwise have been

profitably produced because of rig capacity shortages and reduced drilling efficiency is also a

matter of concern from a socio-economic perspective. A number of resources are time-critical in

that their recovery depends on the use of existing infrastructure, and irreversible losses may be

incurred. But the authorities - who take a rather different (more long-term) view of the trade-off

between current and future production - are perhaps also more open to the counterargument that

drilling fast is less important than drilling correctly, and that incentives tied to drilling speed can

reduce reservoir drainage and thereby resource utilisation. Challenges related to health, safety

and the environment (HSE) can also arise. Another source of concern is that additional

incentives can have the effect of driving up costs on top of already high rates. The question is

whether new incentives will primarily encourage a reallocation of the best equipment and

expertise.

Literature

Corts, K (2000), ‘Turnkey Contracts as a Response to Incentive Problems: Evidence from the Offshore Drilling Industry’, working paper, Harvard University. Corts, K S and J Singh (2004), ‘The Effect of Repeated Interaction on Contract Choice: Evidence from Offshore Drilling’, Journal of Law, Economics, and Organization 20 (1), 2004, 230-260.

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IEA (2008), World Energy Outlook, International Energy Agency, Paris.

Iledare, O. O. and A. Pulsipher. 1999. Sources of change in petroleum drilling productivity in onshore Lousiana in the US, 1977-1994. Energy Economics 21, 261-271. Kellogg, R. 2007. Learning by drilling: Inter-Firm Learning and Relationship Persistence in the Texas Oilpatch. Mimeo. Department of Agricultural and Resource Economics at UC Berkeley, and University of California Energy Institute (UCEI). Kemp, A. and S. Kasim. 2006. A regional model of oil and gas exploration in the UKCS. Scottish Journal of Political Economy 53 (2), 198-221. Norwegian Petroleum Directorate. 2007. The Resource Report 2007. (http://www.npd.no/English/Produkter+og+tjenester/Publikasjoner/Ressursrapporter/2007/coverpage.htm) Ministry of Petroleum and Energy. 2008. Fact Sheet 2008. Norwegian petroleum activity. (http://www.npd.no/English/Produkter+og+tjenester/Publikasjoner/Faktaheftet/Faktaheftet+2008/fakta2008.htm). Mohn, K. 2008. Efforts and Efficiency in Oil Exploration: A Vector Error-Correction Approach. The Energy Journal 30 (4), 53-78. Mohn, K. and P. Osmundsen. 2008. Exploration economics in a regulated petroleum Province. Energy Economics 30 (2), 303-320. Osmundsen, P, A Toft and K A Dragvik (2006), ‘Design of Drilling Contracts - Economic Incentives and Safety Issues’, Energy Policy 34, 2324-2329. Osmundsen, P., T. Sørenes, and A. Toft (2008), ’Drilling Contracts and Incentives’, Energy Policy 36, 8, 3138-3144. Osmundsen, P, T. Sørenes, and A. Toft (2009), ‘Oil Service Contracts – New Incentive Schemes to Promote Drilling Efficiency”, Working paper, The University of Stavanger.

Snead, M.C. (2005). ‘The Economics of Deep Drilling in Oklahoma’. Working paper, Oklahoma

State University.

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Table 1. Summary statistics of the estimating sample Variable Mean Std.dev. Min Max ln(meters drilled per day) -0.257 0.519 -2.123 2.199 Oil company type1 0.173 0.378 0 1 Oil company type 2 0.544 0.498 0 1 Facility drilling experience dummy <10 0.210 0.408 0 1 Facility drilling experience dummy 10-30 0.372 0.484 0 1 Facility type semisub steel dummy 0.918 0.275 0 1 Wellbore status P&A dummy 0.830 0.376 0 1 Wellbore purpose dummy (wildcat) 0.653 0.476 0 1 Barents sea dummy 0.078 0.269 0 1 North sea dummy 0.725 0.447 0 1 Discovery dummy 0.270 0.444 0 1 ln(depth in meters) -0.068 0.423 -2.316 0.658 ln(temperature) 0.363 0.371 -1.089 1.031 ln(temperature)^2 0.269 0.257 0.000 1.187 ln(water depth) -0.203 0.672 -1.423 2.154 ln(oil price lagged) -0.146 0.553 -1.464 0.803 ln(No of exploration wells NCS) -0.007 0.435 -2.788 0.431 t 22.215 8.820 1 40 N = 752.

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Table 2. Econometric estimates with log of drilled meters per day as dependent variable*

Variable Coef. Sd.Err. t P-value Oil company type1 0.051 0.045 1.13 0.259 Oil company type 2 0.010 0.038 0.27 0.787 Facility drilling experience dummy <10 -0.049 0.048 -1.04 0.301 Facility drilling experience dummy 10-30 -0.041 0.033 -1.24 0.215 Facility type semisub steel dummy 0.046 0.063 0.72 0.469 Wellbore status P&A dummy 0.074 0.050 1.46 0.144 Wellbore purpose dummy (wildcat) 0.156 0.041 3.84 0.000 Barents sea dummy -0.243 0.057 -4.30 0.000 North sea dummy -0.074 0.048 -1.55 0.121 Discovery dummy -0.256 0.032 -8.02 0.000 ln(depth in meters) -0.466 0.379 -1.23 0.219 ln(depth in meters)^2 -0.023 0.130 -0.17 0.862 ln(depth in meters)*ln(temperature) -0.103 0.219 -0.47 0.639 ln(depth in meters)*ln(water depth) 0.227 0.206 1.10 0.271 ln(temperature) 0.358 0.407 0.88 0.379 ln(temperature)^2 -0.548 0.223 -2.46 0.014 lnwdept_ln~p 0.057 0.210 0.27 0.788 ln(water depth) -0.440 0.119 -3.69 0.000 ln(water depth)^2 -0.139 0.039 -3.56 0.000 ln(oil price lagged) -0.049 0.032 -1.54 0.124 ln(oil price lagged)^2 0.023 0.058 0.39 0.694 ln(No of exploration wells NCS) -0.014 0.061 -0.23 0.815 ln(No of exploration wells NCS)^2 -0.252 0.061 -4.16 0.000 t -0.096 0.034 -2.83 0.005 t^2 0.005 0.001 3.44 0.001 t^3 0.000 0.000 -2.75 0.006 t*ln(depth in meters) 0.012 0.014 0.86 0.388 t*ln(water depth) 0.006 0.004 1.50 0.134 t*ln(temperature) -0.009 0.016 -0.55 0.584 Constant 0.028 0.313 0.09 0.929 N = 752. R-squared = 0.4619.

* Standard errors are heteroskedasticity adjusted.

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Table 3. Elasticity estimates

Variable Mean t-value. p-valueTemperature -0.237 -0.13 0.895Drilled meters -0.247 -1.54 0.124Water depth -0.237 -3.63 0.000Oil price lagged -0.055 -1.66 0.096No of exploration wells NCS -0.010 -0.17 0.864TC 0.021 7.17 0.000