Fracturing/pressure pumping

Deep Convolutional Network Improves Completion Design

The authors develop a methodology that calculates the mechanical specific energy using real-time drillstring acceleration signals directly.

The Deep Convolutional Neural Net architecture developed.
The Deep Convolutional Neural Net architecture developed.

Maximizing stimulated natural and hydraulic-fracture networks is a primary concern for economic production from a horizontal shale gas well. Geomechanical facies and preexisting fractures in each stage are identified based on similarities in formation characteristics. This often requires analyzing large volumes of drilling, logging-while-drilling (LWD), and measurement-while-drilling (MWD) data. In the complete paper, the authors develop a methodology that calculates mechanical specific energy (MSE) using real-time drillstring acceleration signals.

Introduction

The Marcellus Shale Energy and Environmental Laboratory (MSEEL) aimed to improve stimulation efficiency by collecting, integrating, and analyzing real-time LWD and MWD data.

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