Production optimization of hydrocarbon fields is a complex task, due to the high number of processes involved and their synergy. It is therefore challenging to manage an integrated production system, comprising the reservoir and the production wells, the gathering system, and the process plant. The optimization process is driven by input data subjected to uncertainties due to variability, observational errors, and lack of up-to-date measurements. As a consequence, the aforementioned uncertainties are carried out through the simulation process up to final results.This work presents a workflow for oil & gas production optimization under uncertainties, that is structured in three phases. In the first phase, the integrated optimization process with powerful evolutionary algorithms takes place. The result is an optimized field configuration characterized by a deterministic production increase in respect to the initial production. After having identified the uncertain input variables in the optimization process, a Monte Carlo simulation for the optimized field configuration is performed. In this second phase, the input data are modelled through statistic distributions, and the obtained result is a probabilistic distribution of the production increase. The third step is a detailed analysis of the system constraints to evaluate the behaviour of the optimized configuration. In addition, a methodology to choose an alternative setting of the production field, that allows to increase the system reliability, is presented.The integrated workflow has been applied on a case study. The results obtained show that is extremely important to identify and quantify the uncertainties involved in a hydrocarbon production system for the evaluation of its potential production, and to verify the compliance with its constraints. Applying a configuration when a high degree of uncertainty is present may be hazardous and unsafe.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.

Production Optimization is one of the most complex and multi-disciplinary task in the oil & gas industry from an operational point of view. Optimization involves surface asset all along its production life and requires a continuous improvement process. Improvements, modifications, and temporary upsets in surface facilities during operation phase create the necessity to manage and optimize production scenarios with a more tight time-frame.Technology improvements have enabled a widespread use of integrated simulation models for a better asset management to be fully combined with measured field data. This paper shows a dedicated workflow for surface facilities -gathering system and process plant -production enhancement and management using an advanced optimization technique based on a biogenetic algorithm.The main feature of the proposed workflow is the ability to control many variables simultaneously according to the system constraints even with complex, conflicting, and non-direct interconnections and objectives to be reached. The workflow and the optimization approach are included in a wider integrated tool for production management, called rabbit™ -Risked Algorithm for Biogenetical Balance Integration Tool. Other features of this tool, such as transient phenomena and risk analysis evaluations, complete the ability of the tool to support the production and operation management. This paper will provide a useful description of how the tool can contribute in definition of field potential, production optimization and planning, minimizing production losses during planned/unplanned upsets as well as supporting debottlenecking activities.It will provide some case studies of rabbit™ implementations on different oil and gas fields, both on-shore and off-shore, showing benefits on using the integrated workflow.

This paper shows the results of routine field application of an innovative tool for the integrated production optimization of surface facilities based on a biogenetic algorithm. Periodical runs of the tool have been performed in order to evaluate changes in configuration of the system or mitigation of possible production losses, allowing the complete exploitation of field potential at each stage of field production life.The described integrated production optimization tool has the ability to integrate well performances, gathering system calculation, and process plant simulation in a unique environment in order to optimize the field configuration. Thanks to a powerful biogenetic algorithm the searching of the optimum field configuration is performed. Conflicts and interactions between variables, constraints, and operational limitations are balanced and solved holistically by the optimization tool. The operative procedure on the application of integrated modeling production optimization by means of this innovative tool, foresees the run of the model on a periodical basis in order to evaluate possible changes in asset configuration and mitigate production losses associated to workovers or system re-start after possible shutdown.The integrated production modeling was developed for an on-shore oil field and applied regularly to evaluate different scenarios encountered during its operational life and optimize the field configuration at each stage. This paper illustrates different examples coming from the application of the operative procedure for integrated production optimization modeling. First, results are presented on the application of the tool to mitigate production losses during a workover. After a complete field shutdown, different scenarios were analyzed with the support of the optimization tool in order to evaluate the best configuration during the startup phase. Finally, the optimum desired flow rate was calculated for a gas-lift well in order to maximize the global system production. Results of the simulation were shared with the reservoir department for the management of gas injection rate.Short-term production operation management is supported by routinely application of the presented innovative tool. The holistic optimization of complex surface systems allows the complete exploitation of field potential and production losses mitigation at each stage of field production life.

This paper shows a field application of the risked-based approach to an innovative tool based on a biogenetic algorithm for the integrated production optimization of surface facilities. The tool was applied to analyzed different solutions available in the PLEM configuration for a new well tie-in. Uncertainties related to a planned acid job on a well of the system has been taken into account in order to compare the probabilistic results of the analyzed configurations.The described tool has the ability to integrate in a unique environment the whole production system, handling a high number of optimization variables simultaneously. In the first phase, the integrated optimization process with genetic algorithm takes place. The result is an optimized field configuration characterized by a deterministic production increase. In the second phase, after having identified the uncertain input variables in the optimization process, a Monte Carlo simulation for the optimized field configuration is performed. The result is a probabilistic distribution of the production increase. Different PLEM configurations have been compared on the basis of the probabilistic results obtained. The integrated production model was developed for a FPSO treating the production from different subsea oil wells. An additional well was planned to be connected through an existing PLEM to the FPSO. Different configurations were analyzed to connect the well and the deterministic results from the optimization have been compared. Additionally, an acid job was planned for one of the wells in the system. The risked-based approach has been used to compare the different possible configuration to be applied, taking into consideration also the statistical distribution of the productivity index expected after the work-over. The probabilistic results obtained for the different configuration studied were characterized by a defined level of confidence and were used as a support in the decision making process for the tie-in of the new well.The tool is able to take into account uncertainties related to work-over on wells and to evaluate different scenarios with a desired level of confidence. Results from the study show the strength of the presented innovative risked-based approach to the integrated asset modeling, as a tool to support the decision making process.

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