This paper presents the development of a method to provide decision support in the feasibility studies and concept planning phases of oil and gas field development. The objective in developing the methodology was to provide an easy-to-use facility to integrate the production-governing elements of oil and gas fields that capture the integrated production and economic performance of the system. This in a modular and scalable manner includes numerical optimization and uncertainty analyses needed to support engineering decisions. The method follows a series of steps that allow determining the optimal field production profile, drilling schedule, type of offshore structure, pressure support method and selection of artificial lift. The first step consists of creating efficient (low running time) proxy models of the production performance of the field and the costs figures associated with the project. The proxy model of the production performance is based on curves of maximum production rates versus cumulative production and contains all relevant field design features and computation of the most relevant performance indicators to consider in the evaluation. The proxy model to estimate the costs associated with the project is based on linear equations function of production rates and number of wells. The second step is to perform numerical optimization to find optimal production profile and drilling schedule that maximize the net present value of the specific development strategies considered. For the last step, an evaluation of the effect of uncertainties on the results of the numerical optimization using probabilistic methods is performed. The method was applied in a synthetic production system based on public data of Wisting field (currently under development). The field is a remote low-energy oil reservoir located in the Barents Sea. Nine strategies, obtained from the combination of three recovery support methods and three processing facilities, were compared using the net present value as decision factor. The best strategy consists of using a tension leg platform as processing facility and multiphase boosting plus water injection as recovery support method. This strategy generated the highest production and required the lowest costs, resulting in the highest profitability. It was demonstrated that the methodology successfully finds optimal field design features while quantifying the effect of uncertainties.
This paper describes the challenge of managing and optimizing the production of a large land based oilfield with hundreds of ESP-boosted wells arranged in widely distributed well clusters which production converges to major trunklines traversing the field. The Rubiales field, located in the eastern plains of Colombia has challenging features, characteristics and layout that demand effective model-based production optimization and control. The field's gathering system feeds the commingled production to two central field processing plants.The flow of the numerous wells and streams of the network are interdependent as there are no gas separation facilities at the clusters or at any other location in the network between the wellhead sources and the entry to the processing plants. This creates an interdependency of well streams. Thus, any production change at a single well affects the pressure and rate of all other wells in the network and consequently the total field production. The water rate from each individual producing well strongly depends on the drawdown and the stage of depletion of that particular well, and how it is controlled by varying the speed of its ESP. High water cuts of most producing wells and the constraints on water treatment and disposal at the field level dictates a need for frequent readjustment of individual well ESP speed.Adjusting ESP speeds to maximize the field oil production, subject to field water production constraints, must also take into account a variety of additional constraints related to system limitations, ESP performance, power consumption, production operations and reservoir recovery strategy. One cannot rely solely on operational intuition and empirical field practice for individual ESP control. Rather, a modelbased optimization system has been implemented, taking into account all field and well constraints. The implemented system is robust, fast and easy to tune. Furthermore, inflow of heavy and viscous Rubiales oil into the horizontal wellbores is driven by a strong and active aquifer in a highly heterogeneous and permeable reservoir. This results in rapid changes of produced water cut in response to small changes in drawdown, demanding effective tuning of a predictable well inflow function for the purpose of optimization.This paper describes the model-based optimization system employed in the Rubiales field. The system is customized to the large scale and special features of Rubiales, such as the demanding production performance of its wells, the constraints of facilities, and the objective to maximize profit given by production revenues less OPEX.
Summary A methodology is proposed for the production optimization of oil reservoirs constrained by gathering systems. Because of differences in scale and simulation tools, production optimization involving oil reservoirs and gathering networks typically adopts standalone models for each domain. Although some reservoir simulators allow the modeling of inflow-control devices (ICDs) and deviated wells, the handling of gathering-network constraints is still limited. The disregard of such constraints might render unfeasible operational plans with respect to the gathering facilities, precluding their application in real-world fields. We propose using multiple shooting (MS) to handle the output constraints from the gathering network in a scalable way. MS allowed the handling of multiple output constraints because it splits the prediction horizon into several smaller intervals, enabling the use of decomposition and parallelization techniques. The novelty of this work lies in the coupling of reservoir and network models, and in the exploitation of the problem structure to cope with multiple network constraints. An explicit coupling of reservoir and network models is used to avoid the extra burden of converging the equations of the integrated system at every timestep. Instead, the inconsistencies between reservoir and network flows and pressures are modeled as constraints in the optimization formulation. Hence, all constraints regarding both reservoir and network equations are consistent at the convergence of the algorithm. The integrated-production-optimization problem is solved with a reduced sequential quadratic programming (SQP) (RSQP) algorithm, which is an efficient gradient-based optimization method. The MS ability to handle such constraints is assessed by a simulation analysis performed in a two-phase black-oil reservoir producing to a gathering network equipped with electrical submersible pumps (ESPs). The results showed that the method is suitable to handle complex and numerous network constraints. Because of the nonconvex nature of the control-optimization problem, a heuristic procedure was developed to obtain a feasible initial solution for the integrated-production system. Further, a case study compared long-term optimization with short-term practices, where the latter yielded a lower net present value (NPV), arguably because it could not anticipate early water-front arrivals.
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