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Keywords: machine learning
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Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 17–19, 2023
Paper Number: SPE-213058-MS
... intelligence solution provider asia government personnel workflow asset and portfolio management real time system operator production team effective data visualization leak presented surveillance machine learning digital solution provider devon energy spe intelligent energy conference...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 17–19, 2023
Paper Number: SPE-213103-MS
... balance depletion reservoir surveillance machine learning average reservoir pressure seg unconventional resource technology conference avg europe government production logging production monitoring well performance reservoir pressure liquid productivity index productivity index history...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 17–19, 2023
Paper Number: SPE-213061-MS
...) 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...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 17–19, 2023
Paper Number: SPE-213089-MS
... evolution of a closed system. upstream oil & gas workflow inflow performance complex reservoir drillstem/well testing drillstem testing united states government reservoir surveillance machine learning lift type production monitoring forecast average reservoir pressure artificial lift...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 17–19, 2023
Paper Number: SPE-213068-MS
... sustainability north america government united states government social responsibility oklahoma artificial intelligence prediction geothermal application reservoir characterization gradient well integrity longitude interpolation method machine learning integrity geothermal gradient algorithm...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 17–19, 2023
Paper Number: SPE-213080-MS
... but lack fundamentals of reservoir characterization and well-completion information. Machine learning (ML) methods are gaining traction in the oil and gas industry to supplement predicting hydrocarbon production rates, decline rates, and EUR. The ML model can account for the uncertainties associated...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 17–19, 2023
Paper Number: SPE-213095-MS
...Supervised machine learning framework in permeability determination With the various clusters obtained for both reservoir regions, a supervised machine learning framework is implemented to determine the machine learning algorithm which best fits the different clusters of the dataset. Various...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 9–10, 2019
Paper Number: SPE-195220-MS
... and to create strategies for maximizing hydrocarbon recovery during development of unconventional resources where MFs are opened during stimulation treatments. machine learning flow in porous media Upstream Oil & Gas Simulation hydraulic fracturing pixel shale gas Fluid Dynamics natural...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 9–10, 2019
Paper Number: SPE-195179-MS
... to leverage where capital allocation should also be spent to ensure the best spend of capital from an enterprise perspective. Conclusions data mining machine learning Artificial Intelligence waterflooding injector History optimization producer conformance improvement constraint production...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 9–10, 2019
Paper Number: SPE-195185-MS
... employing big data and machine learning to explore the existed production data and geology information to screen the sweet spot from geology point of view. However, current recovery factor of most unconventional reservoirs is still very low (4~10%). A quick production rate decline pushes US operator...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 9–10, 2019
Paper Number: SPE-195181-MS
... greatly reduce the time needed to describe subsequent core. The method demonstrated is not limited to fractures, other lithologic/geologic features could be trained using the same method, which may result in additional efficiencies. machine learning segmentation accuracy neural network...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 9–10, 2019
Paper Number: SPE-195208-MS
... production. To accomplish the optimization of certain lift systems, both data-driven and model-based approaches have been discussed in the previous literature. For example, from the data-driven and statistical perspective, researchers have used machine learning algorithms for ESP monitoring and proactive...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 9–10, 2019
Paper Number: SPE-195226-MS
... the statistical verification of other second order effects on finding the optimal stimulation treatment. fracturing materials hydraulic fracturing proppant intensity fracturing fluid well production Artificial Intelligence white sand Upstream Oil & Gas brown sand machine learning proppant...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 9–10, 2019
Paper Number: SPE-195194-MS
... but they lack the interrelationship between physics-based and data-driven methods as a complementary and a competitor within the era of rise of unconventionals. This study closes the gap and serves as an up-to-date reference for industry professionals. machine learning Upstream Oil & Gas shale...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 9–10, 2019
Paper Number: SPE-195199-MS
... Upstream Oil & Gas source water water sample machine learning produced water discharge viscosity friction reducer Artificial Intelligence concentration viscosity development performance booster hydraulic fracturing water chemistry operation HVFR friction reducer performance...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 9–10, 2019
Paper Number: SPE-195238-MS
... Formation was constructed using publicly available data. This reservoir model was tuned by history matching the production data for the two wells. A data-based regression model was constructed based on machine learning technologies using the same dataset. Both models were coupled in a system to build...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 9–10, 2019
Paper Number: SPE-195197-MS
... for any induced fractures formations globally. Artificial Intelligence circulation machine learning artificial neuron output layer MSE algorithm key drilling parameter neural network drilling parameter alkinani mud loss neuron fracture formation flori hilgedick drilling fluid...
Proceedings Papers

Paper presented at the SPE Oklahoma City Oil and Gas Symposium, April 9–10, 2019
Paper Number: SPE-195204-MS
... machine learning Upstream Oil & Gas Artificial Intelligence correlation oil eur hydraulic fracturing oil production optimization normalized stimulation fluid Reservoir Characterization HCPV completion fluid intensity Niobrara Well cumulative oil production geocluster...

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