Optimization Design Method of “Well Factory” Platform Planning and Wellhead Layout for Oil and Gas Development

Summary “Well factory” refers to the arrangement of multiple or even a large number of similar wells in the same platform and drilling and completing these wells with standardized equipment and a similar operational approach. The well factory mode can effectively handle challenges posed by complex terrains in oceans and mountainous areas and achieve the economical and efficient development of oil and gas resources. However, current design methods of well factory platform planning have not fully considered some important factors including drilling extension limits, 3D well trajectory, and spatial distribution of reservoir target points. Heuristic algorithms often lead to unstable solutions and frequent entrapment in local optima in solving platform planning problems containing multiple drilling mechanism models. A multiobjective optimization design model is built to handle “well factory” platform planning and wellhead layout. The new model is used to focus on minimizing platform quantity, optimizing platform-well matching relationship, minimizing directional drilling total footage, and reducing well trajectory complexity and meanwhile to consider the 3D distribution characteristics of well trajectories under “well factory” mode and the anisotropic features of drilling extension limits. Next, an improved sparrow search algorithm (ISSA) is presented to solve the platform planning model through various update strategies including initial allocation schemes sorting, automatic detection based on the drilling mechanics model, and Levy flight. The results indicate that the ISSA has the advantages of fast escape from local optima, good convergence, and high robustness, and the new model shows excellent flexibility in addressing platform planning problems. After optimization design, the platform number is reduced, the matching relationship between platforms and target points is improved, and total drilling footage and well trajectory complexity are optimized.