Reservoir Characterization: A Machine Learning Approach
This work addresses reservoir characterization for the oil and gas industry, but it is incremental as it applies existing ANN methods to a specific domain problem.
The paper tackled the problem of predicting sand fraction in hydrocarbon reservoirs from seismic attributes by developing two ANN-based frameworks to handle data resolution mismatches, achieving performance metrics like Correlation Coefficient and RMSE.
Reservoir Characterization (RC) can be defined as the act of building a reservoir model that incorporates all the characteristics of the reservoir that are pertinent to its ability to store hydrocarbons and also to produce them.It is a difficult problem due to non-linear and heterogeneous subsurface properties and associated with a number of complex tasks such as data fusion, data mining, formulation of the knowledge base, and handling of the uncertainty.This present work describes the development of algorithms to obtain the functional relationships between predictor seismic attributes and target lithological properties. Seismic attributes are available over a study area with lower vertical resolution. Conversely, well logs and lithological properties are available only at specific well locations in a study area with high vertical resolution.Sand fraction, which represents per unit sand volume within the rock, has a balanced distribution between zero to unity.The thesis addresses the issues of handling the information content mismatch between predictor and target variables and proposes regularization of target property prior to building a prediction model.In this thesis, two Artificial Neural Network (ANN) based frameworks are proposed to model sand fraction from multiple seismic attributes without and with well tops information respectively. The performances of the frameworks are quantified in terms of Correlation Coefficient, Root Mean Square Error, Absolute Error Mean, etc.