Abstract

Field development optimization is one of the most important and complex tasks in the petroleum industry since technical and economic issues should be considered to maximize an asset value. Usually field development plans are optimized first, under a deterministic or probabilistic approach, by numerical simulation or other engineering tools, and then, a base case model served for economic assessment, with some sensitivity analysis.

Complexity in field development optimization is based on uncertainty sources which often arise at the same time, such as strong correlation among oil prices and technical recoverable volumes, development wells, CAPEX and OPEX. Then, uncertainty and dependency between model inputs are not easy to be modeled by conventional tools.

A new probabilistic tool has been proposed to integrate technical recoverable volumes estimation, production performance prediction and economic assessment. Hydrocarbon recoverable volumes are estimated using a volumetric equation set up to run Monte Carlo simulation; thousands of realizations are then captured by the model to build an expectation curve. In addition, type curves from analogous fields and conceptual simulation studies are incorporated to obtain the probabilistic distribution of the parameters that will allow us to predict the production performance of a field under a stochastic approach. Finally, all the output production profiles are integrated to a probabilistic cash flow model based on real options, to assess at the same time, the uncertainty placed on oil & gas prices, CAPEX, OPEX, tax royalties and other economic inputs over profitability indicators such as NPV, IRR, pay out and investment/profitability rate.

The use of real options will allow us to quantify the value of important flexibilities normally neglected in a typical cash flow analysis such as the value of waiting to develop the lease, the possibility of early development, and the abandonment of the project. To achieve this objective, we will use computational techniques such as finite difference and Monte Carlo simulations considering two price models for oil and gas: Geometric Brownian Motion and Mean Reverting Model.

The model was applied to a remote Peruvian oilfield suspected to be under water drive mechanism to optimize its development strategy in early times, by finding the most suitable number of development wells according to field size, and selecting the optimal production rates, increasing recovery factor and delaying water breakthrough, variables that eventually would contribute with maximizing asset value.

In addition, the model was capable to test and identify simultaneously the most sensitive inputs affecting recoverable volumes and asset value, as well as, to account for the risk expressed in expectation curves of technical recoverable volumes, as well as, the expected monetary value.

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