Abstract
This paper describes a practical methodology that links probabilistic play analysis, volumetric calculations, and economical evaluation to portfolio optimization using information from a 3-D seismic cube. In order that geologic chance of success might be calculated using a new probabilistic risking scale, a description of the geological framework is presented both in terms of play characteristics and the dependence on setting between geologic risk factors.
Probabilistic volumetric evaluation and expected monetary value were calculated for each of the targets in a 3-D seismic prospect portfolio by assigning probability distributions to geological, engineering, and economical characteristics. Monte Carlo simulation was also applied so that uncertainty could be modeled for probabilistic input values. Finally, an efficient frontier analysis was generated by building optimal portfolios having various degrees of risk.
Efficient portfolios are built by applying linear programming as an optimization tool. The balance between value and risk is considered for an optimal working interest to be obtained that fits investor constraints and risk attitude instead of a netpresent-value ranking being used. This trade-off maximizes value and minimizes risk for the entire portfolio.
Prospect risk is modeled as chance of loss, or semi-standard deviation, and value is defined as the mean expected monetary value derived from probabilistic economical evaluation.
A new geologic probabilistic risking scale was developed that adds standardization to the Monte Carlo simulation of geologic probability of success. This scale improves on previous methods by taking into account the uncertainty in judgment, as well as the quantity and quality of available information.
The methodology presented in this paper combines optimal portfolio management with probabilistic risk-analysis methodology, thus helping to guide management in evaluating a portfolio of exploration prospects, not just according to their value, but also by their inherent risk. This practical analysis is simple to apply using economical tools such as spreadsheets and Monte Carlo simulation software.
