APPLICATION OF TIMES SERIES ANALYSIS IN FORECASTING FUTURE PERFORMANCE OF A RESERVOIR UNDER WATER INJECTION

Decline Curve Analysis (DCA) and History Matching (HM) are classical methods
used in predicting reservoir performance. While both methods are widely used, they
have certain limitations and strengths. DCA is only applicable for reservoir with
primary drive and assumes that all mechanical conditions of a well remain constant.
HM, on the other hand, is very complex, takes longer time, and require experience.
Hence, a new simpler and faster technique is required. In this work, a technique called
Time Series Analysis (TSA) is proposed for predicting the reservoir performance.
Time series analysis is widely used in predicting future patterns in economics and
weather forecasting, where factors influencing output are too many to consider. Other
examples of the application of time series analysis are prediction of equipment
prognostic and process of quality control. Two types of TSA were tested: Output-Error
(OE) and Box-Jenkins (BJ). Eight models are developed by varying the order of each
models. Two of the best models were chosen based on the resulting normalized root
mean square error (NRMSE) and are compared with the conventional reservoir
forecasting methods. The NRMSE from the selected models, OE (1-2-1) and BJ (1-2-
1-2-1), showed a comparable result with DCA and HM. The result of this study shows
that, TSA has a very good potential for use in reservoir performance prediction under
water injection and hence it can be utilized as alternative reservoir forecasting tool