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Decisions regarding problem conceptualization, search approach, and how best to parametrize optimization methods for practical application are key to successful implementation of optimization approaches within georesources field development projects. This work provides decision support regarding the application of derivative-free search approaches for concurrent optimization of inflow control valves (ICVs) and well controls. A set of state-of-the-art approaches possessing different search features is implemented over two reference cases, and their performance, resource requirements, and specific method configurations are compared across multiple problem formulations for completion design. In this study, problem formulations to optimize completion design comprise fixed ICVs and piecewise-constant well controls. The design is optimized by several derivative-free methodologies relying on parallel pattern-search (tAPPS), population-based stochastic sampling (tPSO) and trust-region interpolation-based models (tDFTR). These methodologies are tested on a heterogeneous two-dimensional case and on a realistic case based on a section of the Olympus benchmark model. Three problem formulations are applied in both cases, i.e., one formulation optimizes ICV settings only, while two joint configurations also treat producer and injector controls as variables. Various method parametrizations across the range of cases and problem formulations exploit the different search features to improve convergence, achieve final objectives and infer response surface features. The scope of this particular study treats only deterministic problem formulations. Results outline performance trade-offs between parallelizable algorithms (tAPPS, tPSO) with high total runtime search efficiency and the local-search trust-region approach (tDFTR) providing effective objective gains for a low number of cost function evaluations. tAPPS demonstrates robust performance across different problem formulations that can support exploration efforts, e.g., during a pre-drill design phase while multiple independent tDFTR runs can provide local tuning capability around established solutions in a time-constrained post-drill setting. Additional remarks regarding joint completion design optimization, comparison metrics, and relative algorithm performance given the varying problem formulations are also made.
Decisions regarding problem conceptualization, search approach, and how best to parametrize optimization methods for practical application are key to successful implementation of optimization approaches within georesources field development projects. This work provides decision support regarding the application of derivative-free search approaches for concurrent optimization of inflow control valves (ICVs) and well controls. A set of state-of-the-art approaches possessing different search features is implemented over two reference cases, and their performance, resource requirements, and specific method configurations are compared across multiple problem formulations for completion design. In this study, problem formulations to optimize completion design comprise fixed ICVs and piecewise-constant well controls. The design is optimized by several derivative-free methodologies relying on parallel pattern-search (tAPPS), population-based stochastic sampling (tPSO) and trust-region interpolation-based models (tDFTR). These methodologies are tested on a heterogeneous two-dimensional case and on a realistic case based on a section of the Olympus benchmark model. Three problem formulations are applied in both cases, i.e., one formulation optimizes ICV settings only, while two joint configurations also treat producer and injector controls as variables. Various method parametrizations across the range of cases and problem formulations exploit the different search features to improve convergence, achieve final objectives and infer response surface features. The scope of this particular study treats only deterministic problem formulations. Results outline performance trade-offs between parallelizable algorithms (tAPPS, tPSO) with high total runtime search efficiency and the local-search trust-region approach (tDFTR) providing effective objective gains for a low number of cost function evaluations. tAPPS demonstrates robust performance across different problem formulations that can support exploration efforts, e.g., during a pre-drill design phase while multiple independent tDFTR runs can provide local tuning capability around established solutions in a time-constrained post-drill setting. Additional remarks regarding joint completion design optimization, comparison metrics, and relative algorithm performance given the varying problem formulations are also made.
With current economic realities, now is the time to produce "more with less". Exception Based Surveillance (EBS) helps eliminate waste in an engineer's day, by removing unnecessary analysis and allowing the engineer to focus on the highest value tasks. For the surveillance of oil wells, an efficient and effective way to achieve this is to use an automated Exception Based Surveillance System, often augmented by data-driven well rate estimates. Specifically, this paper provides examples from Shell operations in Gabon, Malaysia, and the Netherlands on how EBS systems have been set up to address day to day production challenges. The multiple EBS Systems to be described here have been achieved via the tight integration of real-time data in Well, Reservoir and Facilities management (WRFM) workflows and the automation of complex calculations and rule sets. This paper also describes the WRFM "Next Generation surveillance tool" (NGT) currently being rolled out in several Shell assets (Clinton 2016). The work described here regarding enhanced Exception Based Surveillance Systems and integrated Portals go beyond just deploying tools. To be sustainable and value adding over existing practices, the introduction of these systems requires the transformation of roles, processes and tools to fully and efficiently leverage and gain value from now mature
The aim of this work is to develop and perform proof of concept on a digital process technology for real time gas production control from wells with gas coning or high gas oil ratio (GOR) problem. If proven successful based on the pilot trial designed here, full field deployment on all candidate oil wells will be carried out with this technology in a later phase of the project. This Artificial Intelligence based High GOR Well Control (acronym iGOR), technology encompasses the following. Design of a real time well GOR control system based on smart algorithm. The control solution will prevent high GOR/gas breakthrough, thus minimizing gas production without or minimal impact to oil production rates.Optimize well performance by executing real time well control considering changing well condition. The control solution allows for early detection and response to changing well conditions, and thus maximizing liquid production. The fundamental technology of measuring pressure drop across a restriction and using it to control flow stream is not new. However, the application of this for controlling well production, GOR and sub-surface drawdown is unique. Hence the need to prove iGOR technology for robustness before integrating with existing infrastructure. The current operation in Field B is presented and gas breakthrough control concept is explained. The closed loop process control narrative of iGOR is presented with the AI advisory. The ability of iGOR technology to control GOR in production against several case scenarios is validated using dynamic process simulation in candidate wells. For pilot trial, functional specifications are defined. The iGOR technology supports upstream oil and gas industry initiatives to deploy digital and automation technology in achieving operating expenditure reduction, unplanned production deferment prevention and maximizing production from fields.
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