AI-Driven Oil Reservoir Analytics & Flow Prediction
The client approached us with a non-trivial task of building an intelligent system to predict oil reservoir parameters. They needed a solution to classify rock properties into reservoirs or non-reservoirs and predict well flow rates, dealing with complex, heterogeneous, and often incomplete geological data.
A leading energy exploration company tasked with maximizing extraction efficiency. They faced significant challenges in accurately characterizing rock properties and predicting well output due to complex, incomplete, and heterogeneous geological data.
Advanced Data Processing
A data pipeline that cleans, normalizes, and integrates disparate datasets including core samples, logging results, and rock properties.
Predictive Modeling Engine
Use of unsupervised clustering (K-Means) for rock classification and supervised ensemble models (XGBoost, CatBoost) for flow rate prediction.
Visualization Dashboard
Interactive reporting tools built with Matplotlib and Seaborn to present complex geological insights in an intuitive format for engineers.
Data Heterogeneity
Merging incomplete and inconsistent datasets from various sources into a unified framework for analysis.
Geological Complexity
Accurately modeling the non-linear relationships between rock properties and oil flow in diverse reservoir types.
Model Validation
Ensuring the AI predictions were reliable enough to justify multimillion-dollar drilling decisions.
We leveraged deep data analysis and advanced ML techniques. Our team employed K-means clustering to classify rock properties and utilized XGBoost and CatBoost models to predict well flow rates. We rigorously validated these models and provided comprehensive visualizations, turning raw geological data into actionable drilling insights.
