AI Hackathon

Participants receive a comprehensive dataset with historical PLT logs associated with water and oil holdup measurements, Q values, and other Open Hole (OH) and Cased Hole (CH) logs, Pressure, Perforation, inclination, formations, and the well x,y,z data for training their machine learning models in well # 1. Participants also get a testing dataset without well # 6 for participants' ability to self-evaluate their models. At the same time, you would predict the production (PLT output) for wells # 2, 3, 4, and 5. Keeping in mind that the blind test is going to be among one of these four wells. The AI Hackathon committee keeps some data hidden for fair machine-learning model validation. The Participants would also recommend the best location to target drilling new wells and briefly explain the selected target. The Participants must write a report about the algorithms used, the parameters and hyperparameters, and the feature engineering (input data, augmented data, data created out of the given data), the model results (Mean Absolute Error (MAE)) and the run time of the models), also provide a discussion and observation for each predicted well, and the location selection of the new target well to drill.

The objective of the Hackathon is to understand these main things below:

a. The ability of AI to predict the production profile within a particular area from one well to another well.

b. The ability of AI to identify inter-well communication.

c. The ability to identify the optimal new well target location to drill.

Note-1: All these objectives must be honored by participants, and in the report, explain how these objectives have been achieved, MAE, and the run time, and provide recommendations for further actions.

• Mean Absolute Error (MAE): MAE is calculated as the average of the absolute errors between predicted and actual values. The error is the difference between the actual value and the predicted value. Therefore, MAE would be considered the primary criterion for ranking the models.
   
• Model Run Time: This is the time needed to run a model on the validation data set. Model Run Time is the second criterion when two algorithms deliver the same MAE. Therefore, it might be used in the MAE tie between different participant models.

Participants must exclusively use the data provided for model development, and unethical practices will result in disqualification. The Hackathon is open to universities, research institutions, and organizations, fostering inclusivity and collaboration. Documentation of the model development process is required.

• Participants must use the provided training data to develop their machine-learning models. External data sources are not allowed. However, data augmentation or creating new data from the provided data is welcomed. If additional data is required, the participants should request it via email to the listed addresses in the Contacts section.
• The Hackathon will be open to universities, research institutions, organizations, and individuals. Teams may consist of individuals or groups from these entities or in collaborations with others.
• Participants must submit their predictions for the testing data within the specified timeframe.
• The use of any unethical or unfair practices, including plagiarism or data leakage, will result in disqualification.
• Participants are encouraged to document their model development process and submit an explanation of their approach and predictions as described in the Data, Usage, Reporting, and Objective section Note-1.

• Qt: Total Flow Rate
• Qo: Oil Flow Rate
• Qw: Water Flow Rate
• Qg: Gas Flow Rate
• Hw: Water Holdup
• Ho: Oil Holdup
• Hg: Gas Holdup
• Pressure
• Temperature
• Identify the area to drill a new well between these two wells

The Hackathon offers prizes, recognition certificates, and potential collaboration opportunities to three winners and high-performing participants, incentivizing cutting-edge contributions to the oil and gas industry. This would include the team members who participated in the winning submission. The registration fees for GOTECH 2024 will be waived-off for winners.

Winners will be announced by Thursday, 25 April 2024.

Note: The winners to bear all the travel and accommodation-related expenses.

Based on the GOTECH 2024 program time availability, the top three winners might be invited to present at GOTECH.

Frequently Asked Questions