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Title: A pattern-based approach to waterflood performance prediction using knowledge management tools and classical reservoir engineering forecasting methods
Authors: Emre Artun; Maurice Vanderhaeghen; Paul Murray
Petroleum and Natural Gas Engineering Program, Northern Cyprus Campus, Middle East Technical University, Kalkanli, Guzelyurt, TRNC, Mersin 10, 99738, Turkey
QRI International, LLC, 2 Houston Center, 909 Fannin, Suite 2200, Houston, TX 77010, USA
Upstream Digital Intelligence, 1855 Post Oak Dr, Houston, TX 77027, USA
Abstract: An efficient and rapid workflow is presented to estimate the recovery performance of an existing vertical-well, pattern-based waterflood recovery design using knowledge management and reservoir engineering in a collaborative manner. The knowledge management tool is used to gather production data and calculate pattern-based recoveries and injection volumes by defining pattern boundaries and allocating annual well injection/production volumes in a systematic manner. Classical reservoir engineering forecasting methods, namely, a combination of oil cut versus cumulative recovery performance curves, and decline curve analyses are applied to forecast the performance of the waterflood pattern of interest. Extrapolating established trends of oil cut vs. recovery for each pattern quantified future performance assessments. Time is attached to the performance by introducing liquid rate constraints. Forecasting using both constant and declining liquid rates differentiated the impact of deteriorating reservoir pressure and oil-cut trends on individual pattern oil rate forecasts thus defining current efficiency of each pattern. [Received: November 20, 2014; Accepted: September 22, 2015]
Keywords: waterflooding; predictive data analytics; performance prediction; knowledge management; decline curve analysis; carbonate reservoirs; pattern flood; dashboards; pressure monitoring; reservoir engineering forecasting; waterflood recovery design; oil reservoirs; oil recovery; reservoir management; oilfields.
Int. J. of Oil, Gas and Coal Technology, 2016 Vol.13, No.1, pp.19 - 40
Date of acceptance: 22 Sep 2015
Available online: 25 Jul 2016