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a-deep-ensemble-super-learner-to-predict-reservoir-wettability

The application of a deep ensemble super learner to establish a relationship between wireline logs and NMR T2LM and to predict NMR T2LM. This predicted log could subsequently be used with a developed methodology and mathematical model to predict reservoir wettability.

a-novel-deep-ensemble-super-learning-approach-in-predicting-reservoir-wettability

The application of a deep ensemble super learner to establish a relationship between wireline logs and NMR T2LM and to predict NMR T2LM. This predicted log could subsequently be used with a developed methodology and mathematical model to predict reservoir wettability.

aapg_ml_for_beginners

Machine learning for beginners - Application to reservoir characterisation. A course for UTP AAPG Student Chapter. Kindly download the data and the Anaconda software for this tutorial

cohort-1

Tutorials on the application of artificial intelligence in the petroleum industry

coursera_capstone

drilling-fluid-lost-circulation

The project evaluates causal effects in the prediction of mud loss during drilling activities. Model agnostic metrics, PFI and Shapley, are used to analyse each feature to understand their global implications in predicting mud loss. Several supervised machine learning models are used to predict the mud loss using these selected features.

feature-selection

Feature Selection is a critical data preprocessing step in machine learning which is an effective way in removing irrelevant variables thus reducing the dimensionality of input features. Removing uninformative — or even worse, misinformative — input columns helps to train a machine learning model on a more generalised data with better performances on new and unseen data. In this repository, eight feature selection techniques paired with the gradient boosting regressor model were evaluated based on the statistical comparison of their prediction errors and computational efficiency in reservoir characterisation.

ibm-ai-engineering

ibm-cloude

ielts-sample-material

Download relevant sample questions on all sections of the IELTS exams that will help you pass the exams successfully

machine-learning-with-python-by-ibm

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

oil-simulation

project-walkthroughs

recovery-factor

research-methodology-questions-utp

A collection of past question for research methodology exams organised by UTP

reservoir-characterisation

Prediction of various reservoir propeties

slide-templates

Collection of creative slides that can bring your presentation to life

smart-well-completion

In this study, we proposed a novel machine learning-based approach to predict real-time zonal rates using downhole data. The approach involves using eight machine learning models and permutation feature importance as a model-agnostic metric to identify relevant features.

spe-hackathon_2021

This hackathon is about the application of machine learning in geothermal energy modelling. Notebooks and some materials relating to this hackathon is shared.

static-reservoir-modelling

Static reservoir modelling using Gullfaks field data

study-with-dani-ml-notebooks

Welcome to Study with Dani's YouTube Notebook Repository.All of the notebooks you see here have been presented on my YouTube channel, which can be viewed at the link below. https://www.youtube.com/channel/UCn1O_4_ApzbYwrsUdRoMmOg/ Be sure to like, comment and subscribe to the channel.

wireline-plot

Wireline Plotting Tool

wqu

WorldQuant University Scientific Computing and Python for Data Science

youtube

This repository hosts all the notebooks used for the tutorials on Study with Dani