TITLE:
Stochastic Modeling and Assisted History-Matching Using Multiple Techniques of Multi-Phase Flowback from Multi-Fractured Horizontal Tight Oil Wells
AUTHORS:
Jesse D. Williams-Kovacs, Christopher R. Clarkson
KEYWORDS:
Stochastic Modeling, Assisted History-Matching, Quantitative Flowback Analysis, Rate-Transient Analysis
JOURNAL NAME:
Advances in Pure Mathematics,
Vol.9 No.3,
March
29,
2019
ABSTRACT: In this paper, the methods
developed by[1] are used to analyze
flowback data, which involves modeling flow both before and after the
breakthrough of formation fluids. Despite the versatility of these techniques,
achieving an optimal combination of parameters is often difficult with a single
deterministic analysis. Because of the uncertainty in key model parameters,
this problem is an ideal candidate for uncertainty quantification and advanced
assisted history-matching techniques, including Monte Carlo (MC) simulation and
genetic algorithms (GAs) amongst others. MC simulation, for example, can be
used for both the purpose of assisted history-matching and uncertainty
quantification of key fracture parameters. In this work, several techniques are
tested including both single-objective (SO) and multi-objective (MO) algorithms
for history-matching and uncertainty quantification, using a light tight oil
(LTO) field case. The results of this analysis suggest that many different
algorithms can be used to achieve similar optimization results, making these
viable methods for developing an optimal set of key uncertain fracture parameters.
An indication of uncertainty can also be achieved, which assists in
understanding the range of parameters which can be used to successfully match
the flowback data.