Kriging Unconventional Production Decline Rate from Geological and Completion Parameters

In this extended abstract we propose novel kriging based technique for forecasting and uncertainty quantification in unconventional shale reservoirs. Our technique is data driven; we start from all available reservoir data including high dimensional sets of hydraulic fracturing and geological parameters, along with hydrocarbon production time series. We use functional data analysis to decompose production time series, into a low dimensional space of functional principal component scores. Which enabled us to transform the forecasting problem from complex rate vs. time into a simple regression problem of predicting functional principal component scores at new well locations. Prediction of functional principal component scores is accomplished with recently developed multivariate dice-kriging method. Entire technique is demonstrated on a real reservoir dataset containing 180 horizontal wells with 28 geological and fracturing parameters.