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.
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Geotechnical Engineering and Engineering Geology