Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea

Abstract

The field development workflow contains numerous tasks involving decision-making processes. The modern machine learning methods, including automatic machine learning (AutoML), reduce the geophysics or machine learning experts’ time required to solve routine tasks. In the paper, we focus on the automated solution of the location of the wells optimization problem, namely, improving the quality of oil production estimation and estimating reservoir characteristics for appropriate wells placement and parametrization, using the same AutoML approach. Ideas of making several parallel or consequent tasks automatically within one framework are arising as Composite AI. We implemented and investigated the quality of forecasting models for oil production estimation: physics equation-based, pure data-driven, and hybrid. CRMIP (Capacitance–Resistance Model Injector–Producer) model is chosen as a physics-related approach. We automated the seismic analysis using evolutionary identification of convolutional neural network structure for reservoir detection to help investigate reservoir characteristics for wells location choice. The Volve oil field dataset was used as a case study to conduct the experiments. The implemented approaches can analyze different oil fields and even be adapted to similar physics-related problems.