Abstract:
We develop an unexplored oilfield valuation model under uncertain exploration outcomes, reservoir conditions,
and oil prices. The exploration outcomes follow a binomial process, while oil prices are represented using the
Schwartz-Smith model. The reservoir conditions are characterized by the joint probability distribution of postdiscovery
parameters estimated using a machine learning approach. The corresponding valuation model is a typical
stochastic dynamic programming problem with a simulation-based reward function. The net cash flow lattice
takes the form of a recombining quadrinomial derived from the discrete representation of the Schwartz-Smith oil
price model. We apply backward induction with embedded real-coded genetic algorithms to the net cash flow lattice
to calculate the oilfield value. The model allows for field abandonment before lease expiration if the remaining
reserve is uneconomical. To improve computational efficiency, we combine the Latin hypercube sampling
and antithetic variates to reduce the variances. The model is implemented in an unexplored oilfield under two
scenarios of oil presence probability: 100% and 75%. In the first scenario, we obtained a mean oilfield value of
US$5.5 million with a CVaR of US$0.79 million, while in the second, we came up with a mean of -US$0.77 million
and a CVaR of US$31.74 million.
