Summary
We conducted a practical case study that aims at estimating the shapes and parameters of probability distributions for key cost, time, and activity performance inputs required by a risk, resource, and value simulator to conduct a stochastic valuation of a new exploration asset.
We analyzed a sample of 73 shallow offshore fields in Australia retrieved from a global field-by-field database that includes reserves, production profiles, financials, valuation, breakeven prices, ownership, and other key metrics for global oil and gas fields, discoveries, and exploration licenses.
The reviewed facilities concepts include 40 steel platforms, 2 concrete gravity-based developments, 10 projects with extended-reach drilling, 4 floating production, storage, and offloading (FPSO) vessels, and 17 subsea tiebacks. The aggregate capital expenditure (Capex) of projects in the sample during 1965–2015 is USD 99.1 billion (in nominal terms), which is commensurate with the total asset size of the Australian offshore petroleum industry.
We estimate probability distributions for all full-cycle parameters required to generate Monte Carlo Capex, operating expenditure (Opex), and production profiles. In particular, these include facility Capex per unit peak, development-phase duration and scheduling rules, drilling expenditures per barrel of oil equivalent (BOE), cost of exploration and appraisal wells, Opex/Capex ratio, abandonment cost ratio, fraction of hydrocarbons produced yearly at plateau, fraction of hydrocarbons remaining at end of plateau, terminal production rate, and fraction predrilled wells. Most of the aforementioned were found to be log-normally distributed.
The paper illustrates a practical application of a simple, yet robust, work flow relying on real industry data to assess the value, risks, and uncertainties of an exploration prospect. It can be seamlessly extended to other basins because of a rich coverage of the online field-analogs database.
