Machine Learning-Driven Prediction Technology for Oil and Gas Reserves Abundance

As the petroleum industry enters the era of big data, many oil companies have gradually accumulated massive data resources through global exploration and development processes. The key to fully leveraging these big data lies in finding suitable data analysis and processing techniques to identify patterns within existing data and make predictions in both time and space. This, in turn, guides research and decision-making in the field of oil and gas. Machine learning algorithms constitute a category of algorithms that automatically analyze data to uncover patterns and utilize these patterns to predict unknown data. This category includes many mature algorithms. It is a challenge faced by big data analysts to select the most suitable algorithm in practical work. This study, based on a large commercial oil and gas database, evaluates approximately 30,000 oil and gas reservoirs in major oil and gas basins worldwide as the fundamental assessment units. Various machine learning algorithms, namely the Gradient Boosting Decision Tree (GBDT), Bayesian Ridge Regression, and Adaptive Boosting, are employed to predict the geological reserves abundance of oil and gas reservoirs. The paper compares the performance of these algorithms in predicting geological reserves abundance of oil and gas reservoirs, analyzes the advantages and disadvantages of different algorithms, and fine-tunes each algorithm during training to align with the characteristics of the dataset. Ultimately, the Gradient Boosting Decision Tree algorithm demonstrates satisfactory accuracy in predicting the geological reserves abundance, providing inspiration and guidance for the application of machine learning in global oil and gas resource assessment, new project evaluation, and favorable target selection.