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Collaborating Authors
Oil and gas executives across the North American shale sector are continuing to come to the table and negotiate a steady stream of deals to consolidate portfolios. During the second quarter, the deal making amounted to more than $33 billion in mergers and acquisitions, according to data from Enverus. The energy-focused analytics firm said last month in its quarterly review the combined figure represents more than 40 deals, with seven of them topping $1 billion each. The third quarter has so far not seen any announced transactions surpass the $1-billion mark. Instead, most deals struck in July were between mid-sized and small US-based operators.
Abstract European refining is undergoing a transformation, faced with whittling margins and increased competition from other geographical areas, many have had to shut down. Different alternatives have appeared within Europe, being one of the preferred to integrate refining and trading and not using them solely to buy crudes and sell products, upon request. Based on the case of topping refineries in Europe, closed due to regulatory measures and low margins, three distinct examples of new uses of refinery assets for trading purposes are presented: a) Shutting down a refinery in safety conditions and starting it up in batches, only when the refining margin is positive. To assure the profitability of this operation, the margin is blocked using the futures market, so it is not exposed to a possible deterioration when the cargo is distilled. b) In a permanently closed refinery, the storage capacity could be used for trading operations. bi) Taking advantage of crude oil price fluctuation, especially when the market is in contango. Stocks are increased but it allows to improve the crude oil basket price for other refineries of the company or even to increase the processability of some kinds of crude oils. bii) Using refineries’ facilities to blend surplus streams, instead of selling the components separately, with a lower profit. Such as the case of gasoline blending, which has a higher number of components. This strategy allows the company to have more flexibility in scheduling turnarounds. Real examples where the three options mentioned above were held in different scenarios will be mentioned. For refineries with a strategic location, for example, near West Africa (crude oil exporter and gasoline bullish market) and with the objective of increasing the value of surplus streams for other refineries belonging to the same group, these alternatives are better suited for isolated refineries than the larger-scale solutions implemented in Northern Europe or the conversion to biorefineries being also a hot topic in the region.
- Africa (1.00)
- Europe > Spain > Canary Islands > Tenerife (0.16)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (1.00)
- Energy > Oil & Gas > Downstream (1.00)
- Oceania > Australia > New South Wales > Sydney Basin (0.99)
- Asia > Kazakhstan > Atyrau Oblast > Caspian Sea > Precaspian Basin > Kashagan Field (0.98)
Summary In oil and gas markets, the relationships between the spot and futures prices reveal important opportunities for value creation. When oil prices are in contango (i.e., when futures prices are higher than the expected future spot prices), it may be profitable for a trader to hold oil in storage and enter into a futures contract instead of selling oil in the spot market. The decision to either sell oil in the spot market or use the storage to sell oil in the future is usually challenging because the future spot prices and futures prices are uncertain. In this paper, we discuss the storage trading decisions by use of a realistic example, and we propose an analysis methodology on the basis of a two-factor price process for modeling spot and futures oil prices. The dynamic decision problem, sell spot or sell forward, is analyzed with a forward dynamic optimization algorithm and the least-squares Monte Carlo simulation.
Summary Although a severe drop in commodity prices was expected to adversely affect a financially leveraged producer, the variability of this effect across the universe of exploration and production (E&P) companies during the current downturn, which began in Autumn 2014, surprised industry participants. What factors caused the effect to be magnified for certain companies and muted for others? In examining 71 public E&P companies, we found a moderate correlation between a company's financial leverage and the loss of its equity value. Our study confirms that leveraged producers are exposed to the risk of debt-induced value loss in a downturn. Two other factors were studied for their influence on equity performance: the economics of a company's resource portfolio and the extent of its commodity-price hedging. To examine the effect of resource economics, we analyzed the finding-and-development (F&D) cost of producers and noticed statistically significant differences by hydrocarbon-producing regions. When controlled for the regional effect, the correlation between a company's financial leverage and the loss of its equity value substantially improved. The offsetting effect of a superior resource economics on the debt-induced value loss was evident. For example, the producers operating in the Permian Basin outperformed their similarly leveraged peers operating in the Williston Basin, a less-profitable region. A subset of financially leveraged companies significantly outperformed their similarly leveraged peers. These outperformers, termed here as “Leaders,” showcase a strong alignment between their financing and hedging activities. We observed evidence of the use of a continuous-hedging program through the price cycle by the Leaders. They responded soon to changes in their hedge positions, such as hedge roll-offs, rather than hedging when it is advantageous to do so. A key benefit of such a continuous-hedging program is the dollar-cost averaging of hedged prices, which appears to be an implicit goal of the Leaders. We contend that a leveraged producer must coordinate its hedging and financing policies to maintain an alignment between the hedged volume and the debt load. Endogeneity arises in the relationship between debt and hedges, with each influencing the other. An alignment can be achieved through the implementation of a continuous-hedging program factoring in annual production, operating cashflows, financial leverage, and hedged volumes. This paper takes the hedging debate for a leveraged producer beyond the realm of “to hedge or not to hedge” and addresses the question of “how much to hedge.”
- Financial News (1.00)
- Research Report > New Finding (0.93)
- Research Report > Experimental Study (0.68)
- Energy > Oil & Gas > Upstream (1.00)
- Banking & Finance > Trading (1.00)
- Government > Regional Government > North America Government > United States Government (0.46)
- North America > United States > West Virginia > Appalachian Basin (0.99)
- North America > United States > Virginia > Appalachian Basin (0.99)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- (34 more...)
Summary When exploring nearby prospects in a common area, the outcome of drilling a well can change the chance of success in nearby prospects, affecting their economics and drilling decisions. Here, besides possibly discovering hydrocarbons, a single well could also supply information about other wells. For such a cluster of exploration prospects, which well should we drill first, and which next? More importantly, what is the economic value of this group of prospects? The answers are multidimensional; they depend, at least, on geological dependencies and economic dynamics. Because it takes time to interpret each drilling outcome and update our understanding regarding neighboring prospects, the varying hydrocarbon prices also affect the economics of the upcoming wells. Therefore, our sequence of drilling decisions should consider both geological dependencies and uncertainty in prices. In this paper, we develop a valuation model for a group of interdependent prospects. We use a dynamic programming model that combines the binomial representation of prices with the conditional probability of success or failure at each drilling site. The software implementation of the algorithm accompanies this paper and is a useful valuation and decision‐support system.
- North America > United States (0.46)
- Europe (0.46)
- Information Technology > Decision Support Systems (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.66)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.66)