Integrating Geostatistical Models and Machine Learning for Infill Well Placement and Production Forecasting in Mature Field

Optimizing infill well placement in mature oil fields is complex due to reservoir dynamic complexity and extensive production history.

ombining geostatistical models with machine learning workflows yields quick and effective results. Geostatistical models capture geological and petrophysical properties, while machine learning analyzes and predicts time-dependent variables.

The production model involves history matching and forecasting using a workflow that includes initial production estimation, reservoir pressure prediction, generating missing data, predicting production rates, and model validation. The primary algorithm used is Random Forest.

The saturation model employs XGBoost for modeling lateral-vertical saturation movement and creating a 3D saturation model. This model predicts saturation distribution during history and forecasts future saturation distribution.

The infill location model identifies potential infill well locations and perforation intervals, and forecasts production potential. It uses well filtering, clustering analysis, and pressure estimation to optimize production efficiency..

The integrated approach successfully identifies multiple infill locations without simulation, providing better results than data-driven models alone. Validation of results after each model run and scrutiny of suggested locations are crucial for forecasting production profiles.