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Keywords: machine learning
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Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0051
... characterization of ash layers can be a time-consuming process that leads to wide variations in the interpretations that are made with regard to their presence and potential impact. We seek to use machine learning (ML) techniques to facilitate rapid and more consistent identification of ash layers and other...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0037
... responses for each facies. To overcome such a challenge, machine learning (ML) is helpful to determine characteristic log responses. In this study, we classified the lithofacies by applying ML to the conventional well logs for the volcanic formation, onshore, northeast Japan. The volcanic formation of the...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0006
... evaluation. machine learning artificial intelligence well logging cape vulture main spwla 62 reservoir characterization upstream oil & gas nd annual logging symposium spwla-2021-0006 log analysis norwegian sea communication evaluation resistivity water saturation well 6608...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0033
... network simulation correlation well logging frequency grain size conductivity log interpretation annual logging symposium permittivity nd annual logging symposium dispersion spwla-2021-0033 salinity water-filled porosity machine learning artificial intelligence spwla 62 permittivity log...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0007
... monitoring machine learning log analysis artificial intelligence upstream oil & gas reservoir surveillance wellbore seismic structural geology resolution reservoir characterization well logging borehole imaging color space application color model intensity grassmann spectral distribution...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0036
... Abstract Decades of subsurface exploration and characterisation have led to the collation and storage of large volumes of well related data. The amount of data gathered daily continues to grow rapidly as technology and recording methods improve. With the increasing adoption of machine learning...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0003
... in conventional wireline production logging technologies and potentially also in LWD conditions, when the well is flowing in underbalanced conditions. production monitoring production control artificial intelligence machine learning log analysis annular pressure drilling production...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0059
... developed a physics-driven machine learning-based method to enhance the interpretation of wireline dipole sonic data. However, the LWD scenario introduces additional complexity. This work extends the method to support the interpretation of LWD dipole sonic. An anisotropic root-finding mode-search algorithm...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0071
... ones continue to appear based on emerging Machine Learning techniques. However, most of the available classification methods assume that the inputs are accurate and their inherent uncertainty, related to measurement errors and interpretation steps, is usually neglected. Accounting for facies...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0089
..., resistivity, and etc. We employed 5 various machine learning algorithms for comparison, among which BiLSTM showed the best performance with an R-squared of more than 90% and an RMSE of less than 10. The predicted results can be directly used to calculate geomechanical properties, of which accuracy is also...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0072
... model parameter rock component artificial intelligence water saturation model parameter nd annual logging symposium waxman-smit model volumetric concentration pyrite conductivity image log machine learning upstream oil & gas rock-fabric-related feature electrical conductivity rock...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0045
... well logging log analysis production control wellbore integrity machine learning production monitoring production logging artificial intelligence casing design wellbore design well integrity upstream oil & gas spwla 62 information spwla-2021-0045 nd annual logging symposium...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0030
... rock classification workflows is that it simultaneously improves estimates of both porosity and permeability, and it can capture rock class that might not be identifiable using conventional rock classification techniques. reservoir characterization upstream oil & gas machine learning...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0081
... workflow combines the statistical information obtained from a Machine-Learning (ML) segmentation process with a multiple-layer neural network that defines a Deep Learning process that enhances fractures in a borehole image. These new images allow for a more robust analysis of fracture widths, especially...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0070
... quality checked logs becomes crucial to accurately assess storage volumes in place. Edited curves can also serve as inputs to engineering studies, geological and geophysical models, reservoir evaluation, and many machine learning models employed today. As an example, hydraulic fracturing model inputs may...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0062
... case LWD SGR cannot be run due to certain borehole conditions. This paper compares the results of a slim tool LWD and cuttings SGR data for the first time and concludes on the applicability of each technique. machine learning well logging drilling data acquisition saudi arabia government lwd...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0029
... in some of the incorrectly classified images. Overall, the trained classifier exhibits higher pixel-wise precision and captures the high-resolution heterogeneities more accurately compared to the manual core descriptions. machine learning artificial intelligence well logging log analysis...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0082
... borehole images. Automation is achieved in this unique interpretation methodology using deep learning. The first task comprised the creation of a training dataset of 2D borehole images. This library of images was then used to train machine learning (ML) models. Testing different architectures of...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0069
... Abstract This paper describes an innovative machine learning application, based on variational autoencoder frameworks, to quantify the concentrations and associated uncertainties of common minerals in sedimentary formations using the measurement of atomic element concentrations from...
Proceedings Papers

Paper presented at the SPWLA 62nd Annual Logging Symposium, May 17–20, 2021
Paper Number: SPWLA-2021-0044
... the development of a new producing horizon in a mature field. core analysis tight gas reservoir characterization artificial intelligence machine learning well logging spwla 62 sandstone log analysis complex reservoir information upstream oil & gas porosity elemental...

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