One of the major goals that field planning engineers and decision makers have to achieve in terms of reservoir management and hydrocarbon recovery optimization is the maximization of return on financial investments. This task yet very challenging due to high number of decision variables and some uncertainties, pushes the engineers and technical advisors to seek for robust optimization methods in order to optimally place wells in the most profitable locations with a focus on increasing the net-present value over a project life-cycle. The quest to deliver a good quality advice is also dependent on how some uncertainties – geologic, economic and flow patterns – have been handled and formulated all along the optimization process. With the enhancement of computer power and the advent of remarkable optimization techniques, the oil and gas industry has at hand a wide range of tools to get an overview on value maximization from petroleum assets. Amongst these tools, genetic algorithms which belong to stochastic optimization methods have become well known in the industry as one the best alternatives to apply when trying to solve well placement and production allocation problems, though computationally demanding. The aim of this work is to present a novel approach in the area of hydrocarbon production optimization where control settings and well placement are to be determined based on a single objective function, in addition to the optimization of wells’ trajectories. Starting from a reservoir dynamic model of a synthetic offshore oil field assisted by water injection, the work consisted in building a data-driven model that was generated using artificial neural networks. Then, we used Matlab’s genetic algorithm toolbox to perform all the needed optimizations; from which, we were able to establish a drilling schedule for the set of wells to be realized, and we made it possible to simultaneously get the well location and configuration (vertical or horizontal), well type (producer or injector), well length, well orientation – in the horizontal plane –, as well as well controls (flow rates) and near wellbore pressure with respect to a set of linear and nonlinear-constraints. These constraints were formulated so as to reproduce real field development considerations, and with the aid of a genetic algorithm procedure written upon Matlab, we were able to satisfy those constraints such as, maximum production and injection rates, optimal wellbore pressures, maximum allowable liquid processing capacity, optimal well locations, wells’ drilling and completion maximum duration, in addition to other considerations. We have investigated some scenarios with the intention of proving the benefits of development strategy that we have chosen to study. It was found the chosen scenario could improve NPV by 3 folds in comparison to a base case scenario. The positioning of the wells was successful as all producers were placed in zones having initial water saturation less than 0.4., and all injectors were placed high water saturation zones. Moreover, we established a procedure regarding well trajectory design and optimization by taking into account, minimum dogleg severity and maximum duration for a well to be drilled and completed with respect to a time threshold. The findings as well as the workflow that will be presented hereafter could be considered as a guideline for subsequent tasks pertaining to the process of decision making, especially when it has to do with the development of green oil and gas fields and will certainly help in the placement of wells in less risky and cost-effective locations.

As a Brown Field, located in North Africa. Approximately 95% of Zarzaitine field wells are utilizing Gas Lift as an artificial lift method. The field has a challenging situation to optimize its Oil production; A detailed understanding of the production sys tem thermohydraulic, facility design and the amount of gas injection will ultimately have a major effect on production target. For this purpose, modeling the entire production system was necessary to properly account for the interdependency of wells and surface equipment and determine the system deliverability as a whole by optimizing Gas Lift injection. This paper presents an approach which was introduced for the first time in this field to ensure gas is used efficiently using a multiphase flow simulator for wells and pipelines to model the entire field Production Network in addition to the Oil producing wells including Gas lift mandrels. The model includes 112 Gas Lift wells with a detailed Gas Lift valves system currently on production, each one has been matched against the latest valid well test, Seven Separation Centers, Production gathering pipelines, Production gathering Center and Gas Lift Injection Center. The study has been executed in three major phases: Well Modeling & Calibration, Network Modeling and Gas Lift Optimization. Total Oil production rate has been defined as an objective function during the optimization phase where the total Injected Gas Lift rate for the entire network and for each individual well have been defined as varying parameters; By having a network model calibrated against field data representing the operational conditions of the asset, performing Gas Lift Optimization was the natural next step. Subsequently, by simulating the production system with different Gas Lift Optimization scenarios to maximize Oil production rate under specific surface facilities constraints using the Production Network Model, a better insight of how gas injection rate affects the total production and an understanding of whether a smarter allocation of the current available gas is possible in comparison to the different scenarios has been accomplished. As a result of this Optimization by applying some local and global constraints a 10% Oil production increase has been achieved. This practice has been shown to be successful as predictive technique in a variety of ways specially for such brown fields with more than 60 years of production history. As a next step, to properly manage the real potential of Brown fields, a full field Integrated Asset Model could be created to capture the interaction between the surface and the sub-surface. This model will account for the complex interactions between reservoir, wells and pipelines.

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