An improved model for gas-liquid flow pattern prediction based on machine learning

G Mask, X Wu, K Ling - Journal of Petroleum Science and Engineering, 2019 - Elsevier
The determination of flow patterns is a fundamental problem in two-phase flow analysis, and
an accurate model for gas-liquid flow pattern prediction is critical for any multiphase flow …

A new approach for predicting oil recovery factor during immiscible CO2 flooding in sandstones using dimensionless numbers

D Zivar, P Pourafshary - Journal of Petroleum Exploration and Production …, 2019 - Springer
CO 2 injection is one of the most promising techniques to enhance oil recovery. The most
favorable properties of CO 2 made this method popular and it has been widely used since …

[ΒΙΒΛΙΟ][B] Data Analytics in Reservoir Engineering

S Sankaran, S Matringe, M Sidahmed, L Saputelli… - 2020 - researchgate.net
Data Analytics in Reservoir Engineering Page 1 PetroBrief Data Analytics in Reservoir
Engineering Sathish Sankaran Sebastien Matringe Mohamed Sidahmed Luigi Saputelli …

Study on the classification and formation mechanism of microscopic remaining oil in high water cut stage based on machine learning

Y Zhao, H Jiang, J Li, C Wang, Y Gao, F Yu… - Abu Dhabi International …, 2017 - onepetro.org
The formation mechanism and utilization conditions of the remaining oil in the high water cut
period play significant roles in improved tap** potential and enhanced oil recovery. The …

A new score system using data-driven approach to rank carbonate gas reservoirs in Sichuan Basin

H Li, Z Pan, Y Chen, G Yu, J Zhang, Y Fang… - Journal of Petroleum …, 2023 - Springer
In the early stages of exploration, with only a limited amount of data available, it is difficult to
evaluate a reservoir and optimize the sequence of the development plan. The score system …

Using data analytics on dimensionless numbers to predict the ultimate recovery factors for different drive mechanisms of Gulf of Mexico oil fields

G Talluru, X Wu - SPE Annual Technical Conference and Exhibition?, 2017 - onepetro.org
The ultimate recovery factor is strongly affected by petrophysical parameters, engineering
data, structures, and drive mechanisms. The knowledge of the recovery factor is needed for …

Classification of Wireline Formation Testing Responses Using Unsupervised Machine Learning Methods

P Srivastava, M Bandyopadhyay… - Offshore Technology …, 2022 - onepetro.org
This paper presents a novel technique for planning and execution of Wireline Formation
Testing (WFT) jobs using recent applications of machine learning. WFT measurements …

Reservoir recovery estimation using data analytics and neural network based analogue study

Y Chen, Z Zhu, Y Lu, C Hu, F Gao, W Li… - SPE Asia Pacific Oil …, 2020 - onepetro.org
Reservoir analogue study, which is different with flow physics based prediction methods
such as reservoir simulation, is based on human experience and knowledge from skilled …

Расчет эффекта от перевода добывающей нефтяной скважины в нагнетательный фонд в рамках управления разработкой нефтяным месторождением

ДВ Курганов - Управление большими системами: сборник трудов, 2019 - cyberleninka.ru
Применение алгоритмов машинного обучения (МО) является перспективным
направлением для принятия решений при управлении таким сложным процессом, как …