Evaluation of interwell connectivity of Little Creek Field, Mississipii [sic] from production data

The understanding of geological characteristics and heterogeneity of a reservoir enables better decisions for reservoir development. Statistical methods use universally available injection and production rate data to help evaluate reservoir characteristics and behavior.In this research project, statistical methods typically used to infer communication between injector-producer well pairs in a waterflood reservoir using only production and injection rate data are applied to a CO2 flood. The multivariate linear regression (MLR) technique computes weighting coefficients possibly related to the fraction of the flow in a producer that comes from each of the injectors (Albertoni and Lake, 2002). MLR was applied to the Phase 2 portion of the Little Creek field, Mississippi CO2 flood. Albertoni and Lake use “diffusivity filters” to model the time lag and attenuation between the stimulus (injection) and the response (production), and further modify the model by successive elimination of negative weighting coefficients (SEN) and successive elimination of positive coefficients larger than 1 (SEP). Diffusivity filters do not improve the results for the Little Creek Field. The statistical implications of the SEN and SEP procedures were compared with a less complex simple linear model (SLM) which eliminates the need to make ad hoc assumptions. A statistical hypothesis test (P-Value test) was carried out to determine the significance of each injector-producer well pair relationship. Well pairs with non-significant relationships are then eliminated from the model. This avoids making statistically questionable assumptions to eliminate injector-producer well pairs with connection strengths (i.e., connections not in the range [0,1]). Recommendations to improve sweep were made using results from the Simple Linear Model with the application of the statistical significance test. Suggestions for future work are also presented