... dynamics torsional vibration mathematics of computing directional drilling geology modeling & simulation geologist drilling operation artificial intelligence machine learning drilling string drill string rpm drillstring design journal conference and exhibition engineering oscillation...

..., these models demonstrate the potential of combining reinforcement learning methods and predictive analytics in oil and gas to optimize real-time inputs and resource utilization in this high-risk environment. artificial intelligence efficiency machine learning reinforcement learning action item...

... of cement over longer periods, especially in difficult or adverse downhole conditions, mainly due to a lack of representative data. Machine Learning algorithms and data driven approaches can help solve these problems and help make informed well-operations decisions. During the operational life of a well...

... a machine learning framework for reservoir simulation to improve decisions regarding well spacing and stimulation design, which are pivotal for maximizing hydrocarbon recovery and reducing development costs. Our approach involved a comprehensive development of advanced optimization techniques...

... modification of the corrosion inhibitor recipe was required due to dynamic pumping times associated with limited injectivity. The models were deployed on the cloud to integrate with stimulation design software. A classic set of regression-based machine learning (ML) models were used for training, including...

... Abstract The use of machine learning (ML) techniques for forecasting in the oil and gas sector is growing rapidly, driven by advancements in data processing and computational power. Traditional methods like decline curve analysis and numerical simulations have long been the preferred approach...

.... This selection process is influenced by several factors such as hole conditions, reservoir rock properties, and downhole dynamics. This paper introduces a machine learning approach that integrates well logging data to enhance depth selection, thereby increasing the likelihood of obtaining accurate and valuable...

... by different individuals at different times, using different data processing tools supporting their interpretation, often leads to inconsistent risk evaluation. In this paper, we demonstrate results from a machine learning (ML) based approach that automatically aligns multiaxial MFL inspection data, enabling...

... Abstract The classification of cutter damage in polycrystalline diamond compact (PDC) drill bits has historically relied on manual dull grading, a process that is time-intensive, subjective, and inconsistent. This study introduces an automated classification approach leveraging machine learning...

... economics. Here we present a novel approach to address this by incorporating an advisory machine learning (ML) model that could be used in real time. The model is capable of recognizing trends to predict when a change in concentration is ideal. The historical stimulation data from more than 250 stages from...

... neural network sedimentary rock deep learning hydraulic fracturing machine learning conductivity hydraulic-fractured unconventional well workflow Introduction Reservoir simulation is a complex and multi-step process, encompassing everything from initial geological modeling...

... reservoir geomechanics hydraulic fracturing pressure prediction technology conference workflow pressure channel deep learning proppant concentration geological subdiscipline paper calculation cnn symposium hydraulic fracturing machine learning dataset architecture accuracy dimension...

... rock wellbore design wellbore integrity reservoir characterization adaptive procedure medape analytical solution concentration machine learning accuracy clastic rock rock type mudstone dataset analytical model shale sample drilling fluid ptt upstream pressure experiment pressure...

... algorithms and data analytics approaches were applied to interpret emissions data reported in the GHGRP, covering petroleum and natural gas assets from U.S. upstream sector. The approach included machine learning (ML) techniques such as gradient boosting models and deep learning to capture emission patterns...

... intelligence asia government production operation machine learning digital solution provider production operation team solution provider devon energy leak asset and portfolio management real time system operator production team effective data visualization presented surveillance personnel...

..., reduce deferred production, and extend the life of lift equipment. upstream oil & gas drillstem/well testing drillstem testing workflow inflow performance complex reservoir well performance artificial lift system production monitoring forecast reservoir surveillance machine learning...

... centrifugal separators using data analysis and machine learning (ML) techniques. A comprehensive literature review is conducted to collect the available downhole separator performance data. Experiments and Computational Fluid Dynamic (CFD) simulations are the techniques used by the researchers...

... Abstract Machine learning application in the oil and gas industry is rapidly becoming popular and in recent years has been applied in the optimization of production for various reservoirs. The objective of this paper is to evaluate the efficacy of advanced machine learning algorithms...

...) and microstructure (Scanning Electron Microscopy). Wireline measurements include the triple combo and the sonic logs. Principal Component Analysis and K-means (as unsupervised machine learning algorithms) were applied to both datasets to cluster and classify different rock types. In parallel, the petrophysical...

... america government machine learning integrity geothermal gradient algorithm evaluation technique social responsibility reservoir characterization gradient well integrity longitude interpolation method interpolation geothermal energy shortest distance Introduction Geothermal energy...