An Efficient Method to Control Oilfield Wells Through Advanced Field Management Ensemble Based Optimization Capabilities

Abstract

Field development optimization is a crucial and important problem considering economic and physical constraints. Advanced technologies such as smart wells and completions, and well performance optimization, allow for maximizing recovery without the need of additional drilling activities and all associated costs. This type of strategy applies to mature and challenging heterogeneous reservoirs with water and gas coning, and to low permeability and gas condensate deposits, and others. The presented method elegantly considers all field constraints and delivers predictive optimal well control settings taking into consideration reservoir uncertainties and offers optimized development strategies while minimizing on risk and cost. We will present a new field development technology to optimize well performance in both oil and gas fields. The methodology allows for simultaneous optimization under uncertainty of multiple wells using surface constraints and subsurface flow control devices. The method is very fast and uses a combination of gradient and stochastic methods. Starting from an ensemble of realizations of the reservoir model, the best well control settings are searched using a steepest ascent gradient search method, where the search direction can be approximated using the cross covariance of the ensemble of objectives predicted using cloned reservoir simulations for a user defined future time horizon. The workflow solution employs several levels of computational complexity reduction combining advanced hardware architecture and localization algorithms to speedup optimization procedures and cover wider range of spatial uncertainty. We present two examples that include control of surface liquid rates of producers and injection rates of injectors as well as controlling the flow control valves of producer and injector wells to demonstrate both efficiency and robustness of the optimization method. The method presented is very robust and offers with the highest confidence optimal field developments making best use of the most advanced software and hardware technology. The novel presented process results in significant cost reduction on drilling and other operational activities.

Review of Python Applications in Solving Oil and Gas Problems

“…Abbas and Mustapha [32] implemented the ensemble model predictive control algorithm using Python with the simulator. Avila [33] developed a python simulator to model well PI degradation and optimize intelligent well completions (IWC) according to the specific operational philosophy of a deepwater Gulf of Mexico asset.…”