Learning Where to Drill: Drilling Decisions and Geological Quality in the Haynesville Shale
We generally attribute the increasing productivity of U.S. shale oil and gas wells to firms learning about the production process, or how to drill. They may instead be learning about the spatial distribution of the resource, or where to drill. These two types of learning have very different implications for future supply as learning how to drill expands the resource base, but learning where to drill simply accelerates depletion. To understand whether firms are improving productivity by learning where to drill, I incorporate learning about the spatial distribution of the resource into a dynamic discrete choice model of firms’ drilling decisions. I estimate the model using a rich geospatial dataset incorporating leasing, drilling, and production outcomes in Lousiana’s Haynesville shale. Estimates suggest that while firms are getting better at where to drill, the implied future decreases in productivity are likely to be mild.
Anatomy of a Shale Boom: Optimal Leasing and Drilling with Costly Search
U.S. shale plays tend to first see an initial land rush as firms lease minerals, followed by a long delay before drilling picks up. Based on the characteristics of the mineral leasing process and descriptive statistics from South Texas’ Eagle Ford shale, I argue that this is due to search frictions in the market for mineral rights. I construct a dynamic, general equilibrium model of firms’ joint leasing and drilling decisions when costly search for leases is required, and I characterize the equilibrium path of leasing and drilling using continuous time optimal control methods. The model shows that along the optimal path of leasing and drilling, firms accelerate leasing activity to avoid high search costs when unleased acreage becomes scarce. This dynamic does not arise in a frictionless market unless there is uncertainty in price. In addition to leasing, I also include technological change and a capital-intensive oilfield services sector. With the addition of these two features, the model can explain the qualitative dynamics of shale development in South Texas’ Eagle Ford shale.
Global LNG Pricing Terms and Revisions: An Empirical Analysis
Published in The Energy Journal. Download
Asian long-term contracts for LNG are generally thought to index LNG prices to oil prices. This should mean that LNG and oil prices are cointegrated. However, statistical tests do not bear this out for Japanese prices. To resolve this puzzle, I examine 16 Japanese, South Korean, Taiwanese and Spanish LNG import price series and allow for multiple, unknown structural breaks. This resolves the puzzle, and I provide estimates for the timing of breaks and the underlying average pricing terms. I relate these to count, volume and duration data on long-term contracts. This paper complements existing work on gas market integration, which largely ignores how discrete changes in oil-indexed long-term contracts will affect empirical relationships.
Employment Impacts of Upstream Oil and Gas Investment in the United States
Published in Energy Economics. Download
We use dynamic panel methods at the state level to understand how the increase in exploration and production of oil and natural gas since the mid 2000s has impacted employment. We find robust statistical support for the hypothesis that changes in drilling do, in fact, have an economically meaningful and positive impact on employment. The strongest impact is contemporaneous, though months later in the year also experience statistically and economically meaningful growth. Once dynamic effects are accounted for, we estimate that an additional rig count results in the creation of 31 jobs immediately and 315 jobs in the long run. Robustness checks suggest that these multipliers could be even bigger. Our results imply that the national impact of upstream investment remains small, perhaps due to the sector’s small size and inter-state migration. (with Peter R. Hartley, Kenneth B Medlock III, and Ted Temzelides)
Decomposing Crude Price Differentials: Domestic Shipping Constraints or the Crude Oil Export Ban?
Under revision at The Energy Journal. Download
Over the past decade the U.S. domestic crude benchmark, WTI, diverged considerably from its foreign counterpart, Brent. Some studies pointed to the crude oil export ban as the main culprit for this divergence, but pipeline capacity was also scarce during this time. To understand the drivers of domestic crude oil discounts, we estimate the extent to which transportation constraints can explain price differentials. We find that scarce pipeline capacity explains half to three quarters of the deviation of mid-continent crude oil prices from their long-run relationship with Brent crude. We are unable to find evidence that refining constraints contributed significantly to this differential. This implies that the short-run deleterious effects of the export ban may have been exaggerated. (with Gregory B. Upton Jr, LSU)