Production Forecasting in Unconventional Resources using Data Mining and Time Series Analysis

Abstract

Abstract Production forecasting in shale reservoirs is a challenging task because of the complex influences of geology, lithology, stimulation practices, etc. The large well count makes history matching and forward simulation particularly time consuming and laborious. In such a context, it is important to consider alternative methods, and to this end, we have developed two new methods of forecasting production. The first method uses data mining techniques, which allow the analysis of large quantities of data to discover meaningful pattern and relationships. These can subsequently be used for prediction. Some common data mining tools are neural networks (NN), genetic algorithms (GA), and self-organizing maps (SOM). Our method uses NN for predicting the future performance of a shale gas well based on historical production data of the previous year. The decline in production is captured during the NN training process and applied to the production data during the forecasting phase. The model is simple, elegant and fast and is able to forecast production in an unconventional play with reasonable tolerance. The second method uses time series analysis. It the trend, changes in value, rate of decline, and correlation with the past to generate a rapid and accurate forecast. The stock markets use this technique, and it is safe to say that if it can predict the stock ticks, then it can yield good results on a fluctuating, but surely declining, production rate. These methods are elegant and fast and are able to forecast production in an unconventional play with reasonable tolerance. They are not data intensive and can also be automated to be applied to a large number of wells, which makes them particularly useful in integrated operations in which a comparison of actual versus predicted behavior would enable operators to quickly identify problem wells for a more detailed investigation. The methods were applied to wells from the Barnett, Bakken, and Eagle Ford plays.