The oil and gas industry needs fast and simple
techniques of forecasting oil and gas production. Forecasting production from
unconventional, low permeability reservoirs is particularly challenging. As a
contribution to the continuing efforts of finding solutions to this problem,
this paper studies the use of a statistical, data-driven method of forecasting
production from liquid-rich shale (LRS) reservoirs called the Principal
Components Methodology (PCM). In this study, production of five different
highly volatile and near-critical oil wells was simulated for 30 years
with the aid of a commercial compositional simulator. Principal Components
Methodology (PCM) was applied to production data from the representative wells
by using Singular Value Decomposition (SVD) to calculate the principal
components (PCs). These principal components were then used to forecast oil and
solution gas production from the near-critical oil wells with production
histories ranging from 0.5 to 2 years, and the results were compared to
simulated data and the Modified Arps’ decline model forecasts. 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 technique eliminates the need for
parameters like the hyperbolic decline exponents (b values) and the task of
switching from one DCA model to another. Also, production forecasting can be
done without necessarily using diagnostic plots. With PCM, production could be
forecasted from liquid-rich shale reservoirs
with reasonable certainty. This study presents an innovative and simple
method of forecasting production from liquid-rich shale (LRS) reservoirs. It
provides fresh insights into how estimating production can be done in a
different way.
Cite this paper
Makinde, I. (2017). Statistical, Data-Driven Approach to Forecasting Production from Liquid-Rich Shale Reservoirs. Open Access Library Journal, 4, e4053. doi: http://dx.doi.org/10.4236/oalib.1104053.
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