INTEGRATED FACILITY PLACEMENT LAYOUT OPTIMIZATION

Abstract

Oil and gas production systems are getting deeper, more complex, and far away offshore where energy companies are targeting further resources. These complexities are transforming the problem of field layout design optimization into a much more pressing need. Optimal facility placement is key in field development planning. However, this process should be integrated with well placement and well trajectory design which poses a real challenge especially early in the field development planning at the concept screening phase. During that phase, multiple concepts need to be assessed within, typically, a tight project timeline. The integrated workflow becomes prohibitively expensive and calls, hence, for a fast and robust method that fulfils the objectives in the context of real onshore and offshore field development planning. Facility placement optimization consists mainly of minimizing the resulting cost while honoring topological complexities and prescribed capacity and trajectory constraints. It consists of selecting the optimal number and location of the different “nodes” comprising the facility and the optimal paths of the pipelines (connections) between nodes. Considering the various pipeline design and layout constraints and associated investment costs, planning and development of production gathering and transmission pipeline networks for oil and gas fields is gaining further importance in field development planning. The optimization of transmission and gathering pipeline networks is conducted to accommodate the encountered topological complexities and significantly reduce total investment cost for the corresponding fields. Although many optimization schemes are developed and widely available in literature, these methods are either prohibitively slow with exhaustive search required, or they do not account for the various topological complexities typically encountered in real scenarios. Thus, the deficiency associated to these optimization schemes becomes drastically more limiting in the case of a concept-select phase of field development planning where many scenarios need to be assessed in a relatively short timeframe. Deterministic optimization methods hit memory and computational limitations for all but trivial scenarios and, hence, fail to address the problem. Recently, genetic algorithms were applied with promising results addressing this challenge. This work presents benchmark comparison between SLSQP, GA, PSO, DA and DE developed evolutionary based algorithms for facility placement optimization coupled with the A* algorithm for pipelines layout. This innovative designed shortest path A* algorithm is introduced to the field of pipeline placement optimization knowing that A* algorithm has been successfully used earlier in other applications such as unmanned aerial vehicle (UAV) and robots motion planning. A benchmark comparison is presented with the Dijkstra algorithm; another algorithm that assures optimal shortest path solution that was recently introduced into this field. This comparison is performed on a varied set of pipeline layout scenarios accounting for different topological complexities and dynamic conditions. The conducted tests show the superiority of the A* algorithm in terms of accounting for the application heuristics and assuring an optimal and efficient solution. This work builds on the developed A* pipeline layout and integrates it into the modular developed and implemented evolutionary-based approach for facility placement optimization through various levels of problem complexity: multiple facility layers, topological complexity and prohibited areas. The proposed algorithms are apt to be used in real field development planning projects and are, to the best of our knowledge, the first published material addressing the problem with the required level of speed, robustness and integration. This work is then extended to cover oil and gas brown fields cases, as these fields are highly spread all over the world; brown fields are already existing fields that required further development after passing through several production phases. The developed algorithm builds on the corresponding existing oil and gas field considering the existing wells, pipelines and nodes while placing new wells pipelines and nodes integrating these two parts together to optimize the usage of already existing facility and maximize the production outcome. Thus the developed scheme shows a superiority and outperformance of the DA-based evolutionary algorithm for facility placement layout optimization covering both greenfield and brownfield scenarios integrating the developed A* based algorithm for pipeline layout optimization. Furthermore, the developed algorithm shows modularity and flexibility tackling real-life cases for various targeted oil and gas fields.