4.7 Article

An integrated machine learning-based approach to identifying controlling factors of unconventional shale productivity

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Editorial Material Energy & Fuels

Hydraulic fracturing-induced seismicity characterization through coupled modeling of stress and fracture-fault systems

Gang Hui et al.

Summary: This work summarizes recent findings on hydraulic fracturing-induced seismicity in the Duvernay shale reservoirs. A coupled model was used to quantify stress and pressure changes and seismicity nucleation during hydraulic fracturing. Five triggering mechanisms were identified in seismicity-frequent areas, providing a foundation for further investigation and mitigation strategies.

ADVANCES IN GEO-ENERGY RESEARCH (2022)

Article Multidisciplinary Sciences

Mitigating risks from hydraulic fracturing-induced seismicity in unconventional reservoirs: case study

Gang Hui et al.

Summary: This paper conducts a comprehensive investigation of risk mitigations from induced seismicity caused by hydraulic fracturing and identifies the optimal region for fracturing operations. A field case study validates the applicability of a comprehensive approach in mitigating seismicity risk.

SCIENTIFIC REPORTS (2022)

Article Energy & Fuels

Machine learning-based production forecast for shale gas in unconventional reservoirs via integration of geological and operational factors

Gang Hui et al.

Summary: A comprehensive machine learning approach was developed to forecast shale gas production by integrating geological and operational factors, with factors such as fluid injection, proppant mass, and well characteristics found to have significant impact on production. The Extra Trees approach demonstrated the highest coefficient of determination R2 at 0.81, showing potential for effective prediction of shale gas production.

JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING (2021)

Article Energy & Fuels

Key factors controlling the occurrence of shale oil and gas in the Eagle Ford Shale, the Gulf Coast Basin: Models for sweet spot identification

Lianhua Hou et al.

Summary: The study systematically examines the key geological parameters controlling the enrichment and productivity of shale oil and gas, including both source and derived parameters. The results indicate that derived parameters for shale are closely related to source parameters, influenced by thermal evolution and tectonic evolution.

JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING (2021)

Article Geochemistry & Geophysics

Investigation on Two Mw 3.6 and Mw 4.1 Earthquakes Triggered by Poroelastic Effects of Hydraulic Fracturing Operations Near Crooked Lake, Alberta

Gang Hui et al.

Summary: A coupled approach of fluid flow and geomechanics was proposed to understand the hydraulic fracturing-induced poroelastic effects triggering earthquake swarms. Results showed that high-permeable damage zones triggered sequential activation of seismic faults. Optimizing injection site selection near existing faults is essential to reduce earthquake risks.

JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH (2021)

Review Engineering, Electrical & Electronic

Real-time stochastic power management strategies in hybrid renewable energy systems: A review of key applications and perspectives

Dana-Alexandra Ciupageanu et al.

ELECTRIC POWER SYSTEMS RESEARCH (2020)

Article Energy & Fuels

Bayesian probabilistic dual-flow-regime decline curve analysis for complex production profile evaluation

Bing Kong et al.

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2020)

Article Energy & Fuels

Insights to fracture stimulation design in unconventional reservoirs based on machine learning modeling

Shuhua Wang et al.

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2019)

Article Thermodynamics

Wind energy integration: Variability analysis and power system impact assessment

Dana-Alexandra Ciupageanu et al.

ENERGY (2019)

Article Computer Science, Artificial Intelligence

Random Forests for Big Data

Robin Genuer et al.

BIG DATA RESEARCH (2017)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Computer Science, Artificial Intelligence

Extremely randomized trees

P Geurts et al.

MACHINE LEARNING (2006)