No abstract

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.

Production optimization is one of the most complex and multi-disciplinary task in the Oil & Gas industry. It involves the surface asset during all its production life and requires a continuous improvement process.Integrated optimization of surface asset, including both gathering facilities and process plant, is a difficult goal to be achieved due to the complexity of such systems and the presence of several variables, uncertainties and operational constraints.Technology improvements have enabled a widespread use of integrated simulation models for a better asset management according with actual field data.Production department team of ENI E&P has developed a tool which maximizes production integrating gathering system simulation with process plant simulation. The global optimum is reached through a powerful genetic algorithm that, according to system constraints, allows to control different variables in a simultaneous way.In fact, steady state simulation environments could bring to unstable field configuration. As a consequence, an updated operative workflow for fluid-dynamic module is needed. This workflow supports the process to select optimized solutions that do not have any criticalities during operation and asset variables upset. This implies that unstable solution are discarded among the investigated ones. Therefore, the obtained maximized production configuration is the global optimum solution (robustness of algorithm) as well as the most stable (fluid-dynamically).Finally, multiphase transient code analysis allows to understand actual flow regime and, if necessary, to identify mitigative actions for management's improvement of investigated asset.The integrated workflow has been applied to a real case study. This presents technical contributions of this work for definition of field potential, production optimization, de-bottlenecking activities, asset management.

The Production Optimization is one of the most complex and multi-disciplinary task in the oil&gas industry from an operational point of view: it involves the surface asset during all its production life and requires a continuous improvement process. In the gathering facilities and process plant engineering design, the optimization is driven by inputs that are subjected, during the asset life, to changes; this fact, coupled to improvements and modifications in surface facilities, creates the necessity to manage and optimize production scenarios with a more frequent 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. In this paper we present a dedicated workflow for surface facilities – gathering system and process plant – production enhancement and management coupled with an advanced optimization technique based on a powerful algorithm. The main feature of this algorithm – and consequently 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 between them and the objective to be reached. It has been proven that this algorithm has a robust practice that detects the global optimum of the feasible area avoiding a premature stop in a local optimum region, a situation quite common in highly-constrained and non-linear optimization problems in oil&gas industry. The technical contributions of the work are the abilities to support operations in: definition of field potential, production optimization and de-bottlenecking activities. In the paper a case study, based on a highly-constrained field with gas treating limitations, is reported and the proposed workflow's results are compared with another integrated optimization available from other software, showing an improved ability to detect global optimum combining well management to the plant capabilities.