Having less data is challenging as it adds to the uncertainty around our understanding. On the contrary, huge volume of existing data with continuous additions poses challenges to incorporate them into subsurface models. Data analytics involving integration of large volume of geology, production and surveillance data at a single platform, analysis of data and data trends depicting a physical process can be used to come up at technically robust results in a short time.

Data driven analysis and forecasting is carried out for a mature steamflood. Firstly, all data including production, injection, pressure, well test, well, static models, petrophysical logs, GIS data are integrated on a single data analytics platform. Trends in data are established and visualized based on the production, injection and geology enabling production performance analysis at various aggregated levels. The established trends are studied to understand the effectiveness of steam injection recovery mechanism, interference and are then forecasted.

Data like PLT and static model information earlier analyzed in silos are integrated and now examined together with production data. Due to data integration at a single platform, connecting and visualizing time series data with spatial location to arrive at certain aggregation or cluster is now rapid and credible. Observations and outcomes from data driven analysis correlate well with the recent years of historical data for well groups with mature production and injection history. For well groups with new and upcoming wells, data driven method may get more biased towards existing but sparse performance thus not capturing the full uncertainty in forecast. Data driven analysis method becomes effective when sufficient data is available to infer trends and results that can be validated with alternate estimation or new data.

This technique finds wide application in brownfields with huge data that are either not analyzed or integrated with other information to develop a holistic understanding of subsurface uncertainties. Aided with effective visualization, this technique provides an alternative methodology to enable rapid decision making.

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