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Summer 2021 Colloquium: Graduate Portfolio in Applied Statistical Modeling

Speaker

Date

Time

Title

August 9 Zoom link: https://utexas.zoom.us/j/98421981060

Meeting ID: 984 2198 1060

Brendan Schuetze

Aug 9

1:00-1:30pm

Associations between Goal Orientation and Self-Regulated Learning Strategies are Stable across Assignment and Course Types, Underrepresented Minority Status, and Gender
Oney Erge Aug 9 1:30-2:00pm

Statistical Modeling of Frictional Pressure Losses in Well Construction


Brendan Schuetze

In this pre-registered replication of findings from Muis and Franco [2009; Contemporary Educational Psychology, 34(4), 306-318], college students (N = 978) from across the United States and Canada were surveyed regarding their goal orientations and learning strategies. A structural equation modelling approach was used to assess the associations between goal orientations and learning strategies. Ten of the twelve associations (83%) tested by Muis and Franco replicated successfully in the current study. Mastery approach goals positively predicted endorsement of all learning strategies (Rehearsal, Critical Thinking, and Elaboration), while performance avoidance goals negatively predicted critical thinking and positively predicted rehearsal. No evidence of the moderation of these associations by gender, underrecruited minority status, or course type (STEM, Humanities, or Social Sciences) was found. The reliability of common scales used in educational research and issues concerning the replication of studies using structural equation modeling are discussed.

 

Oney Erge

This study presents various statistical modeling applications to determine the steady-state and transient frictional pressure losses to improve well construction. Also, the results from the statistical models are compared with physics-based models.

Traditionally, models based on physics including Hagen-Poiseuille flow, Hooke’s law, etc. are used during well construction. These models facilitate the design of the drilling plan and are vital to safely and successfully drilling wellbores. There are major shortcomings, however, to using purely physics-based models. Such as, the models can be inaccurate if the physical dynamics are not fully accounted for. Accurately capturing data to describe these processes can be involved, complex or prohibitively expensive. Therefore, statistical modeling approaches are investigated to assess the advantages and disadvantages over the traditional physics-based models.

Specifically, statistical modeling of standpipe pressure both steady-state and transient are evaluated, in which the steady-state model can predict the magnitude of pressure during drilling and circulation; the transient model can simulate the pressure spikes when flow is initiated. The study assesses neural network, deep learning, Gaussian process, support vector machine, and random forest, which were trained with drilling time-series datasets.

Being able to accurately model and manage the pressure response during drilling operations is essential, especially for wells drilled in narrow-margin environments. Here various statistical models are presented that can more accurately predict the standpipe pressure, leading to safer, more optimized operations.