Machine-Learning Approach Determines Spatial Variation in Shale Decline Curves

Abstract

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 196110, “Machine Learning of Spatially Varying Decline Curves for the Duvernay Formation,” by Aleksandr Bakay, Jef Caers, and Tapan Mukerji, SPE, Stanford University, et al., prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September-2 October. The paper has not been peer reviewed. The two most common techniques for forecasting production performance for a new shale well are decline- (type) curve analysis and machine learning. The complete paper describes an automated machine-learning approach to determine the spatial variation in decline type curves for shale gas production, based on existing data of production, completion, and geological parameters. The methodology allows the user to decide whether the focus should be purely on forecast quality or on a combination of forecast and clustering quality. The resulting model will enable the prediction and uncertainty quantification of production profiles for new target wells or areas in the basin. Methods of Forecasting Production Performance Decline-curve analysis is the most-popular technique for forecasting production performance in shale formations because of the need for fast decisions. The technique involves borrowing decline curves from the closest wells or from wells with similar geological, completion, or fluid properties. The idea of decline-curve analysis is based on the fact that a similar production profile is expected from the closest wells or from wells with similar properties. However, the process is often manual and very subjective. As a result of the approach, each existing well is assigned to a particular cluster of decline curves, each cluster having a certain typical decline curve. Clusters can be spatial or represented in completion variable space. To obtain a production forecast for a new well, the authors use the decline curve from a cluster to which it is believed that the new well will belong, usually sampling from a cluster map. The second approach to forecasting shale production performance is machine learning that focuses on statistical correlations. A statistical model is created that connects decline curves with the same well parameters used in decline curve analysis. Typically, the result is a trained machine-learning model. For a new well, it provides production performance, with or without an uncertainty range. Additionally, maps can be created of forecasted production profiles or total recovered fluid. The objective of the project described in the complete paper was to provide a methodology that creates clusters of decline curves and estimates decline curves for a particular location or set of variables. The intent was to limit the manual aspect of clustering and create a robust work flow.