Study On Production Decline Law Of Shallow-thin Heavy Oil Reservoir In Cyclic Steam Stage Of Horizontal Well

As HDNS technology is more and more widely used in shallow thin super heavy oil reservoir, it is quite important to figure out how to predict its production changing tendency more reasonably and accurately in the production process. The purpose of this study is to summarize the production characteristics, and to study the production decline rule of HDNS simulation in shallow thin super heavy oil reservoir.On the basis of oil production data, production performance of shallow-thin super heavy oil developed by HDNS technology is studied. At the beginning of producing, the well can flow for a few days; oil production of cycle, water recovery rate of cycle, oil steam ratio of cycle and production injection ratio of cycle are summarized. The paper identifies the decline type of HDNS stimulation in the cycle. The decline type of oil production in the cycle is exponential decline type, which is similar to conventional heavy oil reservoir cyclic steam stimulation. By using reservoir numerical simulation method, the mechanism of HDNS is characterized, then the synergistic effect of HDNS is summarized, and this is the reason why super heavy oil by HDNS stimulation is the same as conventional heavy oil cyclic steam stimulation. A semi-analytical model is set up for horizontal well’s productivity in shallow-thin super heavy oil reservoir and its analytical solution is also obtained; the result getting from the semi-analytical model is fitting to the real production data. Using correlation analysis method, factors affecting HDNS stimulation development are represented respectively, and then the main factors that influence the HDNS stimulation development in shallow-thin super heavy oil reservoir are determined by gray correlation analysis method. The cyclic steam injection volume, dissolver injection volume and steam injection rate are very sensitive factors relatively. At the same time, using numerical simulation and multiple nonlinear regressions, a production rising model and a production decline model of HDNS stimulation are established under considering multiple factors.