Linear correlation techniques (LCTs) and partial correlation techniques (PCTs) are well known statistical techniques useful in generating smart workflows for real-time surveillance and monitoring processes, such as waterflooding. In real-time environments, which require short-term analysis (30-day cycling), traditional simulation techniques are less effective and take a considerable amount of CPU time. As an alternative and considering LCTs have been widely applied in the oil industry, to understand relationships between producer and injector wells, LCTs can provide rapid results and even predict expected water breakthrough in producer wells.
This paper describes the use of the Pearson correlation coefficient (PCC), a statistical measurement that is very sensitive to linear relationships between water injection and producing wells, including correlations where one parameter is a nonlinear function of the other. While this technique can show high correlation between some variables that are unrelated and only small correlations between highly related variables, the use of PCC, in many cases, returns measurements concerning the joint behavior of two variables that are important to the decision-making process.
This paper describes how PCC was used to clean noisy data generated in LCTs. This paper also describes the use of the PCT to detect the influence of other variables on the LCT-based results. The study was conducted on a mature, carbonate black-oil reservoir in the Middle East under waterflood. The LCT/PCT results were compared with streamline simulation, and some similarities were found between the streamline simulation well allocation factors and the LCT/PCT results. Regardless of local reservoir conditions, the results showed that LCTs/PCTs are powerful tools that can be used to quickly assess inter-dependencies among producing wells and associated injectors. Use of these tools can enable engineers to make prompt decisions to help prevent water breakthrough and adjust water injection rates.