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.

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Asset optimization has recently become a crucial issue in Oil&Gas industry, considering oil price conjuncture and an increased awareness on environmental aspects. In this paper, an Artificial Intelligence (AI) technique is presented, which is able to manage big dataset to automatically match the entire production model against measured field data. The tool is based on a hybrid in-house developed AI technique, integrating deep neural networks, biogenetical algorithms, commercial simulators and real-time data. The workflow starts with the modeling of the production system through physics-based commercial simulators. A sensitivity analysis identifies the critical variables, which are then randomly varied with a Sobol distribution, exploring the entire solution domain. With these data, a proxy model to the commercial software is generated using an artificial neural network. Finally, the AI tool fed by real-time data is used to match the field behavior: uncertain parameters are modified through a differential evolution algorithm that minimizes the error between calculated and measured variables. The matching parameters are, then, passed to the simulators achieving a field representative model. The tool has been developed considering an operating field in offshore western Africa. The typical uncertain parameters in this kind of field are related to the fluid characteristics, in particular densities and compositions, but also to the physical characterization of the pipelines such as roughness and heat transfer characteristics. The matching process has been performed coupling the proxy model, which is a neural network able to replicate the field behavior, and a differential evolution algorithm as the optimization algorithm. The fitness function to be minimized is a Mean Absolute Percentage Error (MAPE) that represents the distance between the actual field production parameters and the modelled ones. The best configuration of both the neural network and the differential evolution algorithm required a computational time of 6 seconds with a MAPE equal to 2.6%. These results are compared to the one obtained coupling the same differential evolution algorithm with the commercial simulator to perform the matching. The required computational time is equal to about 20 hours (70400s) and a MAPE equal to 2.2%. The big gain with the novel approach is clearly the knocking down of computational time with a comparable error. In this paper, it has been shown how substituting the physical model with a proxy one can give substantial advantages in terms of computational time. In principle, with the velocity of the tool implemented, the matching procedure could be done on a daily basis. This is a breakthrough because it allows having the simulator model always tuned and ready to be utilized.

Asset optimization has recently become a crucial issue in Oil&Gas industry, considering oil price conjuncture and an increased awareness on environmental aspects. In this paper, an Artificial Intelligence technique is presented, which is able to manage big datasets to automatically match the production models and propose an operative solution that maximizes the production. The tool is based on a hybrid in-house developed AI technique, integrating deep neural networks, biogenetical algorithms, commercial simulators and real-time data. The workflow starts with the modeling of the production system through physics based commercial simulators. A sensitivity analysis identifies the critical variables, which are then randomly varied with a Sobol distribution, exploring the entire solution domain. With these data, a proxy model to the commercial software is generated: it consists in an artificial neural network able to replicate the field behavior. Finally, the AI tool fed by real-time data is used firstly to match the field behavior and, successively, to maximize the field production. The tool has been developed considering an offshore oil field. The typical uncertain parameters used in the matching phase are fluid characteristics, in particular densities and compositions, but also some pipeline physical characteristics. The typical optimization parameters could range from choke opening and gas lift to plant operating temperatures. The matching process is performed coupling the proxy model with a differential evolution algorithm whose fitness function is an error function, to be minimized, that represents the distance between the actual field production parameters and the modeled ones. The algorithm finds the solution by varying the uncertain parameters and keeping fixed the known optimization ones. Once the model matches with the production data, the set of uncertain parameters is defined and fixed and the process of optimization can start, by changing the optimization parameters. This is achieved with a second differential evolution algorithm working with the same proxy model. The fitness function in this phase is the oil production, which must be maximized, having as constraint the maximum gas flow rate treatable by the plant. This tool has proven to be able to solve the problem in less than a minute. In this paper, the substantial advantages of substituting the physical model with a proxy one have been shown. With the novel approach, the computational time has been reduced by three orders of magnitude with respect to a classic method. This is a breakthrough because it allows the matching and optimization procedures to be done on a daily basis, which results in having always tuned model and a powerful tool to monitor online the field production.