(589b) Alternatives to Decline-Curve Models for Unconventional Reservoirs: a Case for Data-Driven Discovery of Natural Laws
AIChE Annual Meeting
Wednesday, November 1, 2017 - 3:37pm to 3:59pm
The proposed approach completely bypasses the stipulation of an explicit formula for decline-curve analysis. Rather, a large database of available field data referring to production from unconventional reservoirs is analyzed using multivariate statistical methods, such as principal component analysis (PCA). The analysis suggests that over 96% or 99% of variability in the data can be captured with only one or two latent variables. Therefore, an appropriate model structure naturally emerges from the data, thus eliminating the need to separate production into different flow-related regimes with explicit formulas for corresponding decline curves.
Subsequently, data compression can be used to express all observed production patterns as a simple (linear or nonlinear) combination of just a few basis functions. This combination will constitute the model sought, and the coefficients in the corresponding combination will be the model parameters. In practice, these parameters would be estimated from production data over a limited time period, and the resulting model would predict production over the life of the reservoir, thus estimating ultimate recovery.
The proposed method was tested using cross-validation and found to produce comparable or better predictions than standard (equation-based) methods without the need to manually select different flow regimes for multiphase-flow reservoirs. In essence, the latent variables identified correspond to a new explicit formula that simply does not conform to standard formulas well known in mathematics (e.g. exponentials) but is tailored to describe the data. Parameter estimation using the proposed approach is also simple, as it corresponds to a linear (rather than nonlinear) least-squares problem.
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