Application of Machine Learning to improve the Seismic Characterization inside of Fluvial-Deltaic Reservoir, Zapotal Field, Talara Basin

The development of a Mature Oil field as well as others in the world is a challenging task, and Talara Basin is one of them because of its growing depletion and its structural complexity, the latter made the seismic reflection image a little bit difficult to interpret. However, there are some areas where the seismic data plus well information could be used to get more reliable reservoir characterization using machine learning tools. The relation between different seismic attributes and acoustic impedance derived from well-logs using XGBoost (Extreme Gradient Boosting) regression algorithm is a compelling example of how machine learning could add extra information to predict reservoir sandstones. The aim of this study is to perform a machine learning model throughout the interaction of the extracted amplitude from the nine seismic attribute volumes as a log curves inside the reservoir with acoustic impedance log curve (Ip) in seven wells in the structural block (that contains 10 wells with Ip and 3 out of them are blind wells in the model). As a result, the model gives us the opportunity to predict Ip curves from seismic attributes with higher seismic resolution at each trace of the 3D seismic inside the block. Hence, the acoustic impedance volume from the XGBoost model due to its high resolution could be used to point out isolated sand bodies that will be difficult to predict with stochastic model that only use spaced wells. To be honest, this study does not attempt to replace other workflows of seismic inversion or more robust geostatistical model. On the contrary, the possibility to obtain nonlinear operators from the machine learning algorithm that could learn the anisotropic behavior of the wavefield propagation is the most prominent goal of this study. Furthermore, machine learning models could friendly and swiftly be used as a propagation guide of stochastic seismic inversion. For instance, Zapotal Field, in Talara Basin has a long history with several years of oil production, during its first year of exploitation the wells give enormous volume of hydrocarbon as the development of the fields have been growing, the difficulty to maintain a balance between costs of production by barrels turn out to be difficult which is the main reason why many expenses had to be cut off. Nevertheless, it was the opportunity to use alternative techniques such a machine learning which improve our reservoir models without acquiring commercial software tools that raise the cost, thus there is still remain room for machine learning applications in many areas.