A New Approach to Forecasting Production from Liquid Rich Shale Reservoirs Conference Paper uri icon


  • Abstract The oil and gas industry is in need of rapid and simple techniques of forecasting oil and gas production. Forecasting production from unconventional, low permeability reservoirs is particularly challenging. As a contribution to the ongoing efforts of finding solutions to this problem, this paper presents a new method of forecasting production from liquid rich shale reservoirs called the Principal Components Methodology (PCM). 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. Various factors ranging from ultra-low permeability to multi-phase flow effects have plagued the mission of forecasting production from liquid rich shale reservoirs. Traditional decline curve analysis (DCA) methods have not been completely adequate for estimating production from shale reservoirs. The PCM method enables us to obtain the production decline structure that best captures the variance in the data from the representative wells considered. This new technique eliminates the need for parameters like the hyperbolic decline exponents (b values) and the task of switching from one DCA model to another. It also allows us to forecast production without necessarily using diagnostic plots. With PCM, we were able to forecast oil production from shale reservoirs with reasonable certainty. This study presents an innovative and simple method of forecasting production from shale reservoirs. It provides fresh insights into how estimating production can be done in a different way.

author list (cited authors)

  • Makinde, I., & Lee, W. J.

complete list of authors

  • Makinde, Ibukun||Lee, W John

publication date

  • November 2016


  • SPE  Publisher