Multi-perspective Sand Connectivity Analysis and Optimal Water Injection Deployment

Abstract

JE oilfield was a reservoir with edge-bottom water, and it was in the early stage of development in 2018. It showed insufficient energy of edge-bottom water and rapid decline of formation pressure. In order to maintain high and stable production, it was necessary to study the connectivity of sand body and carry out artificial water flooding. In the absence of dynamic response data of injection-production wells, it was important to make full use of the oilfield preliminary data. Firstly, the connectivity of sand bodies was determined by well correlation, secondly, the distribution of seismic attributes was corresponded to determine the plane distribution characteristics of sand bodies; then, the boundary and internal connectivity of sand bodies were studied by well test analysis; finally, numerical simulation technology was used to optimize the flooding effect from the well candidates of transferring wells to water injection strategy. Through the analysis of cross-well section, seismic attributes and well test data, it was sure that the sand body in JE oilfield has good connectivity, and the sand body was in northwest-southeast direction. The numerical simulation results showed that the main flow direction of injected water was consistent with the direction of sand body distribution, and it was in Southeast direction. Three transferred injection wells are selected and the reasonable injection-production ratio was optimized to be 0.8. Through the sensitivity analysis of increasing the liquid production of the response wells to water injection, it was obvious that increasing the liquid production by 25% could further improve the water flooding effect. The multi-perspective analysis method of sand body connectivity proposed in this paper studied the formation information, seismic attributes and well test data synthetically, clarified the sand body shape and boundary, and defined the sand body connectivity. The multi-parameter sensitivity analysis determined the water flooding effect. It provided an effective method of optimal water flooding deployment to similar edge-bottom water drive reservoirs in the early stages of development.