The Production Performance Evaluation of Hydraulically Fractured Wells in the East Sulige Field Using Machine Learning

Abstract

The paper presents a comprehensive workflow to integrate the machine learning algorithm with the Monte Carlo simulation, and a field example is provided to demonstrate that the proposed workflow could reasonably capture the behaviour of well production data. The workflow helps engineers in learning valuable lessons from their historical operations to optimize the future hydraulic fracturing treatments in the Sulige gas field.