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Atlanta Marriott Marquis, A702
Hosted By:
International Association for Energy Economics
To assess the contribution and consequences of learning about where to drill for supply, I estimate (1) what firms know about how productive a location will be before they drill it, and (2) how what firms learn helps them concentrate drilling in more productive locations. I then assess the quantitative implications of firms' information for the dynamics of productivity and supply: (1) in the short run, is learning sufficient to increase average output per well, and (2) in the long run, how severe are the depletion effects associated with the transition from drilling better to worse locations?
I estimate the model using a dataset I assemble on Louisiana's Haynesville shale, a natural gas-producing area, during the 2003—2016 period. This rich, geospatial dataset draws on several public and private sources and includes each mineral lease, well, and natural gas production stream in the play.
Estimates suggest that firms' initial signals about the spatial distribution of deposits are very noisy, but initial drilling resolves much of the uncertainty. Learning about where to drill may have led to economically meaningful increases in output per well, but the long-run productivity implications of the implied acceleration in depletion appear to be mild.
Single and Bidirectional Economic Dependencies in Energy Systems
Paper Session
Saturday, Jan. 5, 2019 12:30 PM - 2:15 PM
- Chair: Alberto J. Lamadrid, Lehigh University
Business Cycles and Innovation Cycles in the Upstream Oil & Gas Industry: Surviving the Ups and Downs
Abstract
The oil and gas industry prides itself on its ability to adopt, adapt, or create new technology. But by the time a new technology has been tested and enters the market, commodity prices may have collapsed, and innovators fail to capture the value of their innovations. In this research, we develop insights that can help the upstream oil and gas industry—exploration and production companies as well as service companies—better understand oil price and innovation cycles. We conceptualize upstream technology innovation based on econometric analysis, and based on case studies of key upstream innovations. Our econometric analysis shows that E&P companies’ research and development efforts have been reactive. R&D have expanded during periods of rising and high oil prices and have contracted during periods of falling and low oil prices. Research and development results follow a different pattern. These have been the outgrowth of proactive technologically-driven research programs which were largely independent of oil price levels. Full commercialization of such innovations often missed the window for the innovator to capture its maximum value. In other words, development and adoption of major innovations takes longer than the oil price cycle. Upstream innovators need to take this into consideration in planning research and development programs, so they can better estimate the value and maximize the benefits of their R&D investments.Learning Where to Drill: Drilling Decisions and Geological Quality in the Haynesville Shale
Abstract
The productivity of unconventional oil and gas wells in the U.S. has risen dramatically as firms have learned about two aspects of extraction: the production process itself (i.e., how to drill) and the spatial distribution of the resource (i.e., where to drill). Both industry and academic studies have tended to focus on learning how to drill, especially with regards to the sand and water used in completions, but ignored firms’ choices of where to drill.To assess the contribution and consequences of learning about where to drill for supply, I estimate (1) what firms know about how productive a location will be before they drill it, and (2) how what firms learn helps them concentrate drilling in more productive locations. I then assess the quantitative implications of firms' information for the dynamics of productivity and supply: (1) in the short run, is learning sufficient to increase average output per well, and (2) in the long run, how severe are the depletion effects associated with the transition from drilling better to worse locations?
I estimate the model using a dataset I assemble on Louisiana's Haynesville shale, a natural gas-producing area, during the 2003—2016 period. This rich, geospatial dataset draws on several public and private sources and includes each mineral lease, well, and natural gas production stream in the play.
Estimates suggest that firms' initial signals about the spatial distribution of deposits are very noisy, but initial drilling resolves much of the uncertainty. Learning about where to drill may have led to economically meaningful increases in output per well, but the long-run productivity implications of the implied acceleration in depletion appear to be mild.
Optimization of a Prototype Electric Power System: Legacy Assets and New Investments
Abstract
Recently there has been much discussion about whether current electricity market designs provide adequate resiliency and incentives for new investments (Tierney and Palmer, May 8 & 9, 2018; DOE/NETL 2018; DOE Staff Report, Appendic C, pp. 154-155). These are complex issues, affecting the gas market and the uptake of advanced technology, to which careful analysis and modelling can provide insights and suggest sensible steps forward. Here we present optimization model results which balance major issues including the importance of bi-directional economic dependencies between the natural gas market and electric power systems which are becoming more dependent on gas. One question addressed here is what is the economic value of large dispatchable electric power plants within an optimized electric power grid, taking into account gas market interactions.Discussant(s)
Raul Bajo-Buenestado
,
University of Navarra
Sang-Baum Kang
,
Illinois Institute of Technology
Jialin Zhao
,
St. Mary’s University
Benjamin Leiva
,
University of Georgia
JEL Classifications
- L9 - Industry Studies: Transportation and Utilities
- Q4 - Energy