Integrated Production Optimization in Presence of Uncertainties: The Importance of Risk Analysis for Field Potential Evaluation Available to Purchase

Production optimization is one of the most complex and multi-disciplinary task in the oil & gas industry. The high number of variables involved make the optimization process more challenging, since a global sight of the production asset is required. The optimization process is driven by input data subjected to uncertainties due to physical properties variability, instrument errors, and lack of up-to-date measurements. Neglecting the presence of these uncertainties may lead to an hazardous and unsafe optimized field configuration. For these reasons, the optimization process should be performed through a tool able to manage an integrated production system and its inner uncertainties. A workflow for hydrocarbon production optimization under uncertainties is presented in this article. The workflow is basically structured in three phases. In the first phase, the integrated optimization process with a powerful genetic algorithm takes place. The result is an optimized field configuration characterised by a deterministic production increase in respect to the initial production. In the second phase, after having identified the uncertain input variables, a Monte Carlo simulation for the optimized field configuration is performed. The result is a probabilistic distribution of the production increase. The third step is a detailed analysis of system constraints to evaluate the behaviour of the optimized configuration. The integrated workflow has been applied on a case study. The results obtained has shown the importance to identify and quantify uncertainties in an oil & gas production system in order to obtain a solution characterised by a production increase with a high reliability. The proposed workflow is an important tool to evaluate the field potential under uncertainties, optimize production, improve operations and system reliability, and support the decision-making process.