nav[aria-label="Primary Navigation"] { padding: 0; & ul { list-style: none; width: 100%; display: flex; flex-direction: row; justify-content: start; align-items: start; gap: 30px; padding: 0; & li { margin: 0; } & ul li { list-style: none; } } }

JMP in the Upstream

Recent Discussions

JMP in the Upstream 02: Machine Learning Approach to Determine Decline Curves
Original session date: 7 October 2021 Topics covered: Graph Builder, clustering, support vector machines Methodology is inspired by SPE Paper 1961100 - Machine Learning of Spatially Varying Decline Curves for the Duvernay Formation It is understood that heterogeneity exists at all scales in the natural environment. Whether…
JMP in the Upstream 9: Visualizing and predicting pore pressure in Deepwater Gulf of Mexico
Original session date: 5 May2022 Topics covered: Graph Builder, Model Screening, Neural Networks Speaker: Peter Polito (@peter_polito), Sr. Systems Engineer Data: IODP Expd. 308—https://mlp.ldeo.columbia.edu/data/timeline/html/308.html GC955 —https://brg.ldeo.columbia.edu/data/ghp/JIP2/GC955-H/index.html? The concept of…
JMP in the Upstream 8: Deploying XGBoost algorithms in the upstream oil and gas environment
Original session date: 7 April 2022 Topics covered: XGBoost, Hyperparameters, machine learning Speaker: Russ Wolfinger (@russ_wolfinger), Distinguished Research Fellow XGBoost is a (relatively) new machine learning protocol that is only beginning to be deployed in the Upstream Energy and Production industry. This decision…
JMP in the Upstream 7: Optimizing your Python machine learning hyperparameters with JMP
Original session date: 3 March 2022 Topics covered: DOE, space filling, Python, machine learning Speaker: Nick Shelton, Systems Engineer Manager Optimizing machine learning hyperparameters is an important and sometimes fraught process. Variation in a single hyperparameter can greatly alter the efficacy of a model and when…
JMP in the Upstream 06: What's my hydrocarbon? Using spectral data to predict octane rating
Original session date: 3 February 2022 Topics covered: Data import and cleaning, graphing spectral lines, Generalized Regression, and PLS In this episode of JMP in the Upstream, @Bill_Worley (Sr. Systems Engineer) showcases the add-in he co-developed for analyzing spectral data in JMP. He walks us through methods for…
JMP in the Upstream 01: Machine Learning to Predict Hydrocarbon Saturation
Original session date: 2 September 2021 Topics covered: predictive modeling, machine learning, neural networks, XGBoost Data source: https://www.netl.doe.gov/node/6865 In this episode of JMP in the Upstream, Peter Polito (Sr. Systems Engineer/Geologist) walks us through a methodology for predicting downhole hydrocarbon…

Welcome!

It looks like you're new here. Sign in or register to get started.