The capacitance-resistance model(CRM)has been a useful physics-based tool for obtaining production forecasts for decades.However,the model's limitations make it difficult to work with real field cases,where a lot ...The capacitance-resistance model(CRM)has been a useful physics-based tool for obtaining production forecasts for decades.However,the model's limitations make it difficult to work with real field cases,where a lot of various events happen.Such events often include new well commissioning(NWC).We introduce a workflow that combines CRM concepts and kriging into a single tool to handle these types of events during history matching.Moreover,it can be used for selecting a new well placement during infill drilling.To make the workflow even more versatile,an improved version of CRM was used.It takes into account wells shut-ins and performed workovers by additional adjustment of the model coefficients.By preliminary re-weighing and interpolating these coefficients using kriging,the coefficients for potential wells can be determined.The approach was validated using both synthetic and real datasets,from which the cases of putting new wells into operation were selected.The workflow allows a fast assessment of future well performance with a minimal set of reservoir data.This way,a lot of well placement scenarios can be considered,and the best ones could be chosen for more detailed studies.展开更多
文摘The capacitance-resistance model(CRM)has been a useful physics-based tool for obtaining production forecasts for decades.However,the model's limitations make it difficult to work with real field cases,where a lot of various events happen.Such events often include new well commissioning(NWC).We introduce a workflow that combines CRM concepts and kriging into a single tool to handle these types of events during history matching.Moreover,it can be used for selecting a new well placement during infill drilling.To make the workflow even more versatile,an improved version of CRM was used.It takes into account wells shut-ins and performed workovers by additional adjustment of the model coefficients.By preliminary re-weighing and interpolating these coefficients using kriging,the coefficients for potential wells can be determined.The approach was validated using both synthetic and real datasets,from which the cases of putting new wells into operation were selected.The workflow allows a fast assessment of future well performance with a minimal set of reservoir data.This way,a lot of well placement scenarios can be considered,and the best ones could be chosen for more detailed studies.
