Decline Curve Analysis (DCA) is one of the most common tools to predict oil and gas well performance and determine an estimated ultimate recovery based on historical production data. Probabilistic approaches have evolved to provide a measure of uncertainty in such estimates. However, engineers have held onto the belief that such quantification of uncertainty is largely subjective. Consequently there is an innate reluctance to adopt probabilistic methodologies owing to the assumption of prior knowledge of the relevant parameters and reservoir properties distributions.
The objective of this paper is to explicate the development of an improved probabilistic approach to estimate reserves and well performance based on historical production data. This methodology precludes the assumption of prior distributions by adopting a bootstrap workflow that is implemented to construct probabilistic estimates with specified confidence intervals from historical production data. It is a statistical approach to assess the uncertainty of estimates objectively, removing the subjective nature of prior assumptions.
We shall discuss an automated selection criteria workflow for time series and forecast periods resulting in more robust and accurate DCA. The methodology¹ abides by a "more rigorous model-based bootstrap algorithm?? that encapsulates the appropriate steps to preserve the inherent characteristics of a time series data set that show an overall decline trend.
Number of Pages
Looking for more?
Some of the OnePetro partner societies have developed subject- specific wikis that may help.