![AI solution assessment for well abandonment risks and cost estimation [oil & gas]](https://crunch.is/wp-content/uploads/2025/03/documerica-x-EG-R-bUTI-unsplash-1-1-scaled.jpg)
Oil & Gas
USA, Texas
1 PM, 1 ML Engineer, 1 Part-time SME
4 months
Our client provides robust, data-driven solutions for effective liability and risk management in the oil & gas and energy sectors. Their services leverage data integrity, analytical precision, and financial foresight, ensuring responsible asset management and strategic decision-making.
In close collaboration with industry experts we discovered an opportunity and iteratively came up with technical objective of the project. Technical team designed and implemented AI/ML fueled approach to estimate well abandonment risk factors and costs. This helps well’s operators plan budgets and allocate resources within the well abandonment process with greater precision.
Data is a foundation of any AI/ML driven project. Therefore, as the first task our team worked on data enhancement and cleansing, ensuring its consistency and accuracy. Integrated disparate data sources to create a unified dataset for further in-depth analysis.
Prepared extensive and detailed report on available data, possible correlation/casuation insights, data quality and distributions in well plugging and abandonment. Combining deep domain understanding and advance toolset for data analysis and feature engineering created flat-structure like data set suitable for further AI/ML work.
Designed and developed setup for iterative AI/ML and feature engineering experiment. Made an educated decision on preprocessing steps, set of features and model design.
Developed approach successfully used to estimate oil well plugging and abandonment risk factors and costs. The selected approach allowed us to get both – highly confident results and their explanation, which was appreciated and welcomed by domain’s experts.
Our team successfully completed the PoC stage with the client. It demonstrated strong predictive performance with consistent results across various well types in a specific region. The AI well abandonment solution is already scalable, and its API-based deployment supports efficient integration into operational workflows.
AI PoC ready for launch
Utilizing 100,000 completed P&A wells to build and train the model and 50,000 completed P&A wells to predict and check accuracy of the model. I would like to thank the team at Crunch for their hard work over the past few months.
By refactoring the architecture, implementing AI-driven data preprocessing and visualization, and streamlining deployment with DevOps, we delivered a scalable, cost-effective solution.
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