Principal components methodology -A novel approach to forecasting production from liquid-rich shale (LRS) reservoirs

Abstract

With increasing global demand for energy, the importance of unconventional shale oil and gas research cannot be over-emphasized. The oil and gas industry requires rapid and reliable means of forecasting production. Existing traditional decline curve analysis (DCA) methods have been limited in their ability to satisfactorily forecast production from unconventional liquid-rich shale (LRS) reservoirs. This is due to several causes ranging from the complicated production mechanisms to the ultra-low permeability in shales. The use of hybrid (combination) DCA models can improve results. However, complexities associated with these techniques can still make their application quite tedious without proper diagnostic plots, correct use of model parameters and some knowledge of the production mechanisms involved. This work, therefore, presents a new statistical data-driven approach of forecasting production from LRS reservoirs called the Principal Components Methodology (PCM). PCM is a technique that bypasses a lot of the difficulties associated with existing methods of forecasting and forecasts production with reasonable certainty. PCM is a data-driven method of forecasting based on the statistical technique of principal components analysis (PCA).

In our study, we simulated production of fluids with different compositions for 30 years with the aid of a commercial compositional simulator. We then applied the Principal Components Methodology (PCM) to the production data from several representative wells by using Singular Value Decomposition (SVD) to calculate the principal components. These principal components were then used to forecast oil production from wells with production histories ranging from 0.5 to 3 years, and the results were compared to simulated data. Application of the PCM to field data is also included in this work.

This study provides fresh initiatives into how production forecasting from unconventional LRS reservoirs can be done in a different way.

Keywords

Principal components / Liquid-rich shale / Unconventional resources / Production forecasting / Pattern recognition

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