Quantitative Analysis of Inter-Well Communication in Tight Reservoirs: Examples from Montney Formation

Haghshenas, Behjat (Reservoir Diagnostics Inc.) | Qanbari, Farhad (Seven Generations Energy)

OnePetro 

Abstract Well spacing and completion optimization in tight and shale reservoirs is a multi-dimensional task which comprise reservoir rock and fluid characterization, well performance study, inter-well communication analysis, and economic evaluation. Two sources of pressure data for characterization of inter-well communication include offset well pressure monitoring during hydraulic fracturing and controlled communication (interference) tests through staggered production. Both types of inter-well communication tests have become common among the operators in tight and shale reservoirs. However, quantitative analysis tools for interpretation of the test results are in their infancy. The focus of this study is quantitative analysis of pressure interference tests. In this study, an analytical model is developed for quantitative analysis of communication between multi-fractured horizontal wells (MFHWs) using pressure data from production and monitoring well pairs. The governing partial differential equation for the more general case of coupled flow in hydraulic fracture and matrix systems is solved using the Laplace transform. In order to validate the analytical model, the results from the analytical solution are compared against numerical simulation models. The analytical model of this study is applied to two field case from Montney formation. In these cases, a well from a multi-well pad is put on production and bottom-hole pressure of a monitoring well from the same pad is recorded using down-hole recorders. Communications between the wells is quantified using the analytical models of this study. The model of this study serves as a novel and practical tool for quantitative analysis and interpretation of inter-well communication in MFHWs. Integration of the model with other direct diagnostic and measurement tools can provide insight into optimized completion intensity for MFHWs.

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