Abstract
This paper describes the investigation of interwell communication in the Module II of Espadarte field, located in the Campos Basin, offshore Brazil. The field started production in January 2007, seven months before water injection. All active wells are equipped with permanent downhole gauges (PDG) to continuously record flowing pressure and temperature.
As history data became available, PDG bottom-hole pressure was extensively analyzed to infer reservoir properties, estimate its pressure and determine well parameters, such as effective permeabilities, skin, and productivity or injectivity indexes. A routine of injector fall-off tests was implemented. The results obtained were combined with the interpretation of producer pressure build-ups after platform shutdowns. Additionally, in order to evaluate their level of connectivity, the reflect of topside events affecting the injectors was also investigated on the flowing pressure of the producers.
A number of seismic attributes was used to correlate reservoir properties with the degree of connectivity between wells inferred from production behavior. Seismic data was processed through the training of an unsupervised neural network identifying clusters or regions of different seismic properties within the reservoir. A material balance model with interference was developed to represent and evaluate the degree of compartmentalization suggested by bottom-hole pressure analysis and seismic interpretation.
All this information together made possible the identification of regions with poor and good communication between injectors and producers. Conclusions have given support to the history matching of a numerical simulator as well as the optimization of water injection rates. This work also highlights the importance of integration of static and dynamic data, represented by seismic attributes and PDG measurements, respectively, to the monitoring of well and reservoir performance.