1. Business and Innovation Cycles in the US Upstream Oil and Gas Industry Robert Kleinberg, PhD Columbia University Center on Global Energy Policy Boston University Institute for Sustainable Energy Marie N. Fagan, PhD London Economics International, LLC

2. Why don’t upstream companies benefit more from innovation?
Time scale for full development and commercialization of logging while drilling and geo-steering, compared to the oil price cycle
When oil and gas prices are declining and low, “innovation” is frequently invoked as the key to continued petroleum industry viability and profitability
But major technological innovations that require sustained investments of human and financial resources can take a decade or more to mature
In this research, we conceptualize upstream technology innovation based on econometric analysis, and based on case studies of key innovations

3. Data and methods: Econometrics and case studies
Econometric model
What drives innovation effort? To discover what happens to innovation efforts across the oil price cycle, we analyzed R&D spending trends using econometrics:
We used annual upstream R&D spending by large U.S.-based E&P companies and the oilfield service industry across four decades, from the mid-1970s to 2015, encompassing two long oil price cycles
Case studies
What drives innovation results? To understand how innovation results may or may not be related to the oil price cycle, we developed case studies of five important technologies:
three-dimensional seismic surveys
borehole nuclear magnetic resonance
logging-while-drilling and geosteering
rotary steerable drilling
horizontal drilling combined with hydraulic fracturing

4. Oil prices and R&D spending
Data used in econometrics: Boom and bust R&D spending implies stop-and-start innovation efforts
Oil prices and R&D spending
Schlumberger
R&D
Oil Price
E&P R&D
Upstream oil and gas R&D spending seems to be reactive
The service industry, as represented by Schlumberger, seems less reactive

5. Economic theory: Imperfect price reversibility, aka asymmetric effects
Asymmetric regressors
R&D spending could respond more strongly to rising oil prices than to falling oil prices, or vice- versa
A partial adjustment model accounts for long-term versus short term response
LnR&Dt = α + β1Ln R&Dt–1 + β2LnPriceRect + β3LnPriceCutt + εt
Where:
LnR&Dt = natural logarithm of real (2016 dollars) R&D spending in year t
LnPriceRect = cumulative increases in ln(real oil price)
LnPriceCutt = cumulative declines in ln(real oil price)
εt = error term
α, β1, β2, β3 are the coefficients to be estimated

6. Econometric results show symmetrical spending for Schlumberger, asymmetrical spending for E&P group

7. Case studies showed that research-to-commercialization takes longer than the commodity price cycle
The renowned and game-changing combination of horizontal drilling and hydraulic fracturing beginning as a government-sponsored effort in the 1970s

8. Conclusions and strategic advice: Oil price cycles can be relatively long, but they are not long enough!
Our econometric results show that the E&P industry takes a boom-and-bust approach to R&D spending
In contrast, our case studies show that the time needed for research, development, and widespread adoption of important technologies is longer than the oil price cycle
Industry participants who expect to commercialize major innovations should reject boom-and-bust spending in favor of supporting R&D through commodity price cycle
Government-supported and academic research are best directed to long-term goals beyond the planning horizons of petroleum industry participants

9. Appendix: Stationarity of the data used in econometric analysis
Dickey-Fuller unit root test of oil price variable
The estimated Dickey-Fuller coefficient δ is for the lagged ln(Poil) regressor. Rejecting the null hypothesis that δ = 0 in favor of the alternative hypothesis that δ < 0 implies that ln(Poil) is integrated of order zero (i.e., it is stationary). However, its t-statistic of (in absolute value) falls below the Dickey-Fuller 5% critical values. So, though the value indicates a stationary series, our sample size is too small to be 95% sure that the series does not have a stochastic trend.
The value of the coefficient for the deterministic time trend was slightly positive (0.001) but not statistically significant at Dickey-Fuller 5% critical values. This implies there is no significant deterministic time trend.

10. Dickey-Fuller unit root test of R&D spending variable, Schlumberger
Appendix, continued: Stationarity of the data used in econometric analysis
Dickey-Fuller unit root test of R&D spending variable, Schlumberger
Results for SLB, Table 6, showed δ = for the lagged ln(R&D) regressor. Its t-statistic of (in absolute value) falls above the Dickey-Fuller 5% critical values. Thus, we can be 95% sure that the series does not have a stochastic trend.
 The results also show that value of the coefficient for the deterministic time trend was slightly positive (0.001) but not statistically significant based on its t-statistic of (in absolute value) in comparison to Dickey-Fuller 5% critical values. This implies there was no significant deterministic time trend. Thus we can be 95% confident that the Schlumberger data is stationary.

11. Appendix, continued: Stationarity of the data used in econometric analysis
Dickey-Fuller unit root test of R&D spending variable, EIA/IHS Companies
Results showed δ = for the lagged ln(R&D) regressor. Its t-statistic of (in absolute value) falls below the Dickey-Fuller 5% critical values. Thus, we cannot be 95% sure that the series does not have a stochastic trend.
The value of the coefficient for the deterministic time trend was slightly negative (‑0.001) but not statistically significant based on its t-statistic of (in absolute value) in comparison to Dickey-Fuller 5% critical values. This implies there was no significant deterministic time trend.

12. Thank you! Marie N Fagan, PhD Robert Kleinberg, PhD
For more information contact
Marie N Fagan, PhD
Lead Economist and Managing Consultant
London Economics International
Robert Kleinberg, PhD
Columbia University Center on Global Energy Policy
Boston University Institute for Sustainable Energy