Monte Carlo Optimization for Site Selection of Process Facilities in Oilfields Considering Environment

Wang, Bohong (China University of Petroleum) | Liao, Qi (China University of Petroleum) | Zheng, Jianqin (China University of Petroleum) | Yuan, Meng (China University of Petroleum) | Zhang, Haoran (China University of Petroleum) | Liang, Yongtu (China University of Petroleum)



The site selection of the facilities in oilfields is one of the important issues for surface engineers. In the progressive development of oilfields, new wells are explored and developed, and new process facilities (PFs) should be constructed to gather and process the fluid from these new wells. The emission of the PFs will affect the surrounding environment, including water sources, forests, and human settlements. Thus, the environment should be considered as one of the key aspects in the design process of facilities. Different locations of facilities in the oilfields will affect both the construction cost and environmental cost. Thus, a balance has to be found. In addition, the uncertainty of production rate of well fluid poses a great challenge to this problem. To solve the above problem, this paper provides a systematic methodology.

The objective function consists two parts: construction costs and environmental cost. The solving algorithm has involved three layers of looping programming to calculate the value of objective function. First the weather conditions are generated by the Monte Carlo method, then the second loop is for the study areas, and the third loop is for the new facilities locations. After all the loops of iterations are completed, the objective functions are calculated, and the influence of the environment can be evaluated. Finally, the best solution can be obtained.

The effectiveness of the proposed method is demonstrated through a design problem in an oilfield. The candidate locations for PFs are previously determined, and the optimal construction plan is solved by our method. The quantitative influence on the environment to these candidate locations can be evaluated. After determining the coefficient of the construction cost and the environmental cost, the best locations for the process facilities with the lowest total cost can be determined.

A multi-objective model for the site selection of process facilities in oilfields is proposed, which has not been done by existing literatures. The construction cost and surrounding environment are both considered in the model. This work has the potential to serve as a decision-support tool for surface engineers in oilfields.