An applied machine learning approach to production forecast for basement formation - Bach Ho field
Abstract
Oil production forecast is a major challenge in the oil and gas industry. Simulation model and prediction results play an important role in field operation and management. Currently, production forecast problems are resolved mainly by using pure traditional prediction methods. Generally, production forecast by dynamic simulations does not provide reliable results in case where a lot of uncertain parameters remain when the dynamic model is constructed.
In fact, in Vietnam, the dynamic models of fractured reservoirs give unreliable results and differ with actual performance. It is a challenge to build and design reasonable production plans for fractured granite reservoirs in Vietnam. In order to replace the disadvantages of simulation model by different methods, a growing trend of research in the world is constructing predictive tools by using machine learning algorithms.
The paper introduces the applicability of machine learning through the artificial neural network to predict oil production for basement formation - Bach Ho field. The research results show that Artificial Neural Network (ANN) model has improved the ability to predict production with high accuracy.
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