... of Petroleum Engineers upstream oil & gas geologist drillstring dynamics geology united states government drill pipe selection bit design rock type bit selection drilling operation drillstring design geological subdiscipline machine learning spe drilling paper spe annual technical...

..., dispersant, and FA were assigned as input variables for GMDH-LM while CS from the experimental analysis was set as output. Machine learning (ML) findings indicated that GMDH-LM can effectively estimate the CS of OWC. GMDH-LM performed better than backpropagation neural network (BPNN), support vector machine...

... 3 2022 12 7 2022 17 6 2022 5 8 2022 8 3 2023 Copyright © 2023 Society of Petroleum Engineers upstream oil & gas neural network deep learning machine learning apc-lstm artificial intelligence spe drilling drilling operation mechanism experiment...

... network deep learning iran government cement paper cnn machine learning automatic interpretation algorithm company accuracy cement evaluation log confessional matrix midrate artificial intelligence spe drilling conference gas well cement evaluation log Cementing is one of the most...

... section, calibrated DDSs are performed to comprehend the downhole vibration at the reamer and downhole vibration sensors. A surrogate regression model between reamer vibration and sensor vibration is built using machine learning. This surrogate model is implemented in a drilling monitoring software...

... costs. 28 9 2022 23 11 2022 13 11 2022 5 1 2023 Copyright © 2023 Society of Petroleum Engineers upstream oil & gas asia government artificial intelligence china government spe drilling machine learning subsea wellhead drilling operation sub-sea system...

...Erlend Magnus Viggen; Bjørn-Jostein Singstad; Eirik Time; Siddharth Mishra; Eirik Berg Summary The Assisted Cement Log Interpretation Project has used machine learning (ML) to create a tool that interprets cement logs by predicting a predefined set of annular condition codes used in the cement log...

... hydraulics model to determine the threshold of manageable enlargement. The volume of cavings is determined using a machine-learning (ML)-assisted 3D elastoplastic finite-element model (FEM). The model implementation is first validated through experimental data. Next, a full data set from offset wells is used...

... is strong to mitigate such scenarios, and additive information through early recognition of drilled lithology can be of great assistance. neural network machine learning drilling operation upstream oil & gas accuracy reservoir characterization deep learning dnn information drilling...

... wells. The objective of this paper is to apply machine-learning (ML) tools to increase precision of the APB estimation, and thereby improve the fluid and casing design for APB mitigation in a given well. The APB estimation methods in literature involve theoretical and computational tools...

...T. A. Olukoga; Y. Feng Summary There is a great deal of interest in the oil and gas industry (OGI) in seeking ways to implement machine learning (ML) to provide valuable insights for increased profitability. With buzzwords such as data analytics, ML, artificial intelligence (AI), and so forth...

... analysis results showed that the proposed approach can extract the original pulse signal accurately. 16 8 2020 5 10 2020 2 10 2020 23 12 2020 8 9 2021 Copyright © 2021 Society of Petroleum Engineers mwd drilling data acquisition square wave machine learning...

.... (2011) . The present and future risks associated with the system variables were evaluated to generate alarms in the system. Researchers prioritized the alarms according to the severity and provided operator guidelines to mitigate the risk. machine learning managed pressure drilling controller...

... for the MPD system under different noisy scenarios. 30 10 2019 29 6 2020 25 6 2020 29 9 2020 16 6 2021 Copyright © 2021 Society of Petroleum Engineers machine learning production control drilling fluid management & disposal equation of state production monitoring...

... selection and formulation drillstring design artificial intelligence reservoir surveillance production logging drilling fluid property reservoir geomechanics drilling data acquisition drilling fluid management & disposal well planning drilling fluids and materials machine learning drilling...

... wellbore integrity machine learning completion installation and operations fracture complexity oosf fracture artificial intelligence upstream oil & gas communication breakdown pressure wellbore design horizontal-stress anisotropy breakdown angle anisotropy center fracture spe drilling...

... Intelligence drilling fluid selection and formulation drilling fluid management & disposal machine learning drilling fluids and materials friction factor critical reynolds number characterization drilling fluid formulation field drilling operation MSU pressure loss drilling operation real-time...

... oil production normalized per lateral foot. machine learning proppant hydraulic fracturing fracturing materials fracturing fluid Artificial Intelligence regional sand white sand oil production Upstream Oil & Gas completion fracture conductivity well performance conductivity...

... uncertainty in the parameter estimate used to characterize the soils. The statistical uncertainty is quantified using the frequentist and Bayesian approaches, and their results are compared. machine learning Upstream Oil & Gas Artificial Intelligence regression casing design borehole 3 oil well...

... 20 11 2019 24 2 2020 5 2 2020 10 9 2020 Copyright © 2020 Society of Petroleum Engineers Fig. 3 Left: time‐stamp‐wise accuracy. Right: flag‐wise accuracy. Table 2 Confusion matrix for the DL‐based labeling. machine learning Upstream Oil & Gas...