Effective machine learning identification of TOC-rich zones in the Eagle Ford Shale

A new and reliable dual model- and data-driven TOC prediction concept: A TOC logging evaluation method using multiple overlapping methods integrated with semi-supervised deep learning

(2020) Linqi Zhu et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Hydrocarbon source rock evaluation and quantification of organic richness from correlation of well logs and geochemical data: A case study from the sembar formation, Southern Indus Basin, Pakistan

(2020) Haroon Aziz et al.   Journal of Natural Gas Science and Engineering

An improved method for evaluating the TOC content of a shale formation using the dual-difference ΔlogR method

(2019) Linqi Zhu et al.   MARINE AND PETROLEUM GEOLOGY

Data-Driven In-Situ Sonic-Log Synthesis in Shale Reservoirs for Geomechanical Characterization

(2019) Jiabo He et al.   SPE RESERVOIR EVALUATION & ENGINEERING

Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning

(2019) Linqi Zhu et al.   APPLIED SOFT COMPUTING

Machine learning for locating organic matter and pores in scanning electron microscopy images of organic-rich shales

(2019) Yaokun Wu et al.   FUEL

Mineralogical composition and total organic carbon quantification using x-ray fluorescence data from the Upper Cretaceous Eagle Ford Group in southern Texas

(2019) Ahmed Alnahwi et al.   AAPG BULLETIN

SEISMIC FACIES ANALYSIS USING MACHINE-LEARNING

(2018) T. Wrona et al.   GEOPHYSICS

AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

(2018) Aldo Zaimi et al.   Scientific Reports

Rigorous prognostication of permeability of heterogeneous carbonate oil reservoirs: Smart modeling and correlation development

(2018) Alireza Rostami et al.   FUEL

Machine Learning Predicts Laboratory Earthquakes

(2017) Bertrand Rouet-Leduc et al.   GEOPHYSICAL RESEARCH LETTERS

Earthquake magnitude prediction in Hindukush region using machine learning techniques

(2016) K. M. Asim et al.   NATURAL HAZARDS

Estimation of total organic carbon and brittleness volume

(2016) Sumit Verma et al.   Interpretation-A Journal of Subsurface Characterization

Shale elastic property relationships as a function of total organic carbon content using synthetic samples

(2015) Y. Altowairqi et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Support-vector-regression machine technology for total organic carbon content prediction from wireline logs in organic shale: A comparative study

(2015) Maojin Tan et al.   Journal of Natural Gas Science and Engineering

Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information

(2013) Matthew J. Cracknell et al.   COMPUTERS & GEOSCIENCES

Quantitative evaluation of TOC, organic porosity and gas retention distribution in a gas shale play using petroleum system modeling: Application to the Mississippian Barnett Shale

(2013) Maria-Fernanda Romero-Sarmiento et al.   MARINE AND PETROLEUM GEOLOGY

Inversion-based method for estimating total organic carbon and porosity and for diagnosing mineral constituents from multiple well logs in shale-gas formations

(2013) Zoya Heidari et al.   Interpretation-A Journal of Subsurface Characterization

Estimating Total Organic Carbon Content and Source Rock Evaluation, Applying ΔlogR and Neural Network Methods: Ahwaz and Marun Oilfields, SW of Iran

(2011) H. Amiri Bakhtiar et al.   PETROLEUM SCIENCE AND TECHNOLOGY

On the Capability of Support Vector Machines to Classify Lithology from Well Logs

(2010) A. Al-Anazi et al.   Natural Resources Research

Morphology, Genesis, and Distribution of Nanometer-Scale Pores in Siliceous Mudstones of the Mississippian Barnett Shale

(2009) R. G. Loucks et al.   JOURNAL OF SEDIMENTARY RESEARCH