Utilizing Multi-Output Regression and Machine Learning for Reservoir Evaluation

Generally, BCO provides a good option for maximizing oil production in brownfields, allowing access to remaining oil targets after existing completions. Proper BCO maturity, including small reservoir unit analysis, can yield high returns and low-risk projects to acquire cheap oil at low cost. Unfortunately, the lack of appropriate resources, such as potential manpower and budget constraints, can pose challenges to proper BCO analysis. A novel multi-output regression random forest algorithm is used to predict BCO and determine the fluid code of BCO, marking a good start for further implementation of machine learning to address this issue. Multi-output models can predict two or more variables, achieving unified prediction rules and time-saving alternatives. Exploratory Data Analysis (EDA) and necessary data preprocessing provide good input for the algorithm. The algorithm yields two outputs: predicted BCO and fluid code, with a root mean square error (RMSE) of 0.0933 and R² of 0.9619. To better support the logic of model predictions, log curves of the two predicted values were plotted, and the underlying principles behind the predictions were observed. Additionally, a good cross-plot correlation between predicted values and actual output values also helps further validate the results. This study may help further strengthen BCO analysis, providing reliable and highly effective methods for BCO and fluid detection. Furthermore, predicting fluid codes aids in conducting correct reservoir analysis, thus providing time-saving alternatives for better drilling decisions.

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