Machine Learning Based Decline Curve - Spatial Method to Estimate Production Potential of Proposed Wells in Unconventional Shale Gas Reservoirs

Yildirim Kocoglu, Marshal Wigwe, Sheldon Gorell, Marshall Watson

Research output: Contribution to conferencePaper

Abstract

Abstract In the petroleum industry, accurately predicting production potential of undrilled unconventional horizontal wells requires the use of highly complex models and is a constant research in progress. Deterministic approaches such as decline curve analysis (DCA) are used in most cases to estimate production potential due to ease of implementation. The main disadvantage of using DCA by itself is that it requires an existing well to forecast the production, which is very expensive to drill. This paper shows the procedures for building a flexible machine learning based decline curve-spatial method that can be easily used to predict the estimated ultimate recovery (EUR) of newly proposed wells without the requirement of costly data or other time consuming methods. A type of artificial neural network (ANN) called feed forward neural network (FFNN) was used as the machine learning method during this process. In order to achieve this goal, production and well data were collected from pu
Original languageEnglish
DOIs
StatePublished - Jul 20 2020

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