Abstract

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.

The paper also explores other potential advanced analytical techniques to gather knowledge from historical upstream data so as to identify wells within an asset that require some remediation based on real-time performance observations. Cluster analysis could improve engineers’ convenience in analyzing wells by separating them into groups based on decline curve shapes (patterns) and other reservoir properties. We shall define some processes that implement soft computing techniques to aggregate the DCA results with disparate upstream data via a data driven methodology. Pattern recognition and classification are important steps that underpin data mining, and we shall explicate a suite of workflows to enable a well optimization solution.

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