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Found 11 papers, 0 papers with code
Making the black-box brighter: interpreting machine learning algorithm for forecasting drilling accidents
We present an approach for interpreting a black-box alarming system for forecasting accidents and anomalies during the drilling of oil and gas wells.
Forecasting the abnormal events at well drilling with machine learning
We present a data-driven and physics-informed algorithm for drilling accident forecasting.
Machine learning for recovery factor estimation of an oil reservoir: a tool for de-risking at a hydrocarbon asset evaluation
In this work, we present a data-driven technique for oil recovery factor estimation using reservoir parameters and representative statistics.
Application of Machine Learning to accidents detection at directional drilling
We present a data-driven algorithm and mathematical model for anomaly alarming at directional drilling.
Real-time data-driven detection of the rock type alteration during a directional drilling
During the directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20m between the bit and high-fidelity rock type sensors.
Prediction of Porosity and Permeability Alteration based on Machine Learning Algorithms
The objective of this work is to study the applicability of various Machine Learning algorithms for prediction of some rock properties which geoscientists usually define due to special lab analysis.
Gradient Boosting to Boost the Efficiency of Hydraulic Fracturing
We discuss the potential for further development of ML techniques for predicting changes in oil rate after HF.
Reconstruction of 3D Porous Media From 2D Slices
In many branches of earth sciences, the problem of rock study on the micro-level arises.
Deep Neural Networks Predicting Oil Movement in a Development Unit
We present a novel technique for assessing the dynamics of multiphase fluid flow in the oil reservoir.
Data-driven model for the identification of the rock type at a drilling bit
Directional oil well drilling requires high precision of the wellbore positioning inside the productive area.
Driving Digital Rock towards Machine Learning: predicting permeability with Gradient Boosting and Deep Neural Networks
We present a research study aimed at testing of applicability of machine learning techniques for prediction of permeability of digitized rock samples.
