Table of Content

• Introduction
• 3W dataset
  • Structure
  • Overview
• 3W toolkit
  • Structure
  • Reproducibility
• Licenses

Introduction

This repository originated from Petrobras. More details on my contribution can be referred here

The 3W is the pilot of a Petrobras' program called Conexes para Inovao - Mdulo Open Lab. This pilot is an open project composed by two major resources:

• The 3W dataset, which will be evolved and supplemented with more instances from time to time;
• The 3W toolkit, which will also be evolved (in many ways) to cover an increasing number of undesirable events during its development.

Therefore, its strategy is to make these resources publicly available so that we can develop the 3W project with a global community collaboratively.

The goal of this project was to predict the status classification of naturally flowing oil and gas well

3W dataset

To the best of its authors' knowledge, this is the first realistic and public dataset with rare undesirable real events in oil wells that can be readily used as a benchmark dataset for development of machine learning techniques related to inherent difficulties of actual data. For more information about the theory behind this dataset, refer to the paper A realistic and public dataset with rare undesirable real events in oil wells published in the Journal of Petroleum Science and Engineering (link here).

Structure

The 3W dataset consists of all CSV files in the subdirectories of the dataset directory and structured as detailed here.

Overview

A 3W dataset's general presentation with some quantities and statistics is available in this Jupyter Notebook.

3W toolkit

The 3W toolkit is a software package written in Python 3 that contains resources that make the following easier:

• 3W dataset overview generation;
• Experimentation and comparative analysis of Machine Learning-based approaches and algorithms for specific problems related to undesirable events that occur in offshore oil wells during their respective production phases;
• Standardization of key points of the Machine Learning-based algorithm development pipeline.

It is important to note that there are arbitrary choices in this toolkit, but they have been carefully made to allow adequate comparative analysis without compromising the ability to experiment with different approaches and algorithms.

Structure

The 3W toolkit is implemented in sub-modules as discribed here.

All specification is detailed in the CONTRIBUTING GUIDE.

Reproducibility

For all results generated by the 3W toolkit to be consistent, we recommend you create and use a virtual environment with the packages versions specified in the environment.yml, which was generated with conda. First you have to install the Anaconda. Then open an Anaconda Prompt, make sure the current directory is the directory where you have the 3W and run the following commands as needed:

• To create a virtual environment from our environment.yml: $ conda env create -f environment.yml
• To activate the created virtual environment: $ conda activate 3w
• To use the 3W toolkit resources interactively: $ python
• To initialize a local Jupyter Notebook server: $ jupyter notebook

Author's disclaimer

Contributions

We expect to receive various types of contributions from individuals, research institutions, startups, companies and partner oil operators.

Before you can contribute to this project, you need to read and agree to the following documents:

• CODE OF CONDUCT;
• CONTRIBUTOR LICENSE AGREEMENT;
• CONTRIBUTING GUIDE.

It is also very important to know, participate and follow the discussions. See the discussions section.

Licenses

All the code of this project is licensed under the Apache 2.0 License and all 3W dataset data files (CSV files in the subdirectories of the dataset directory) are licensed under the Creative Commons Attribution 4.0 International License.