Klaus Rollmann Techniques for Comparing 4 D Seismic Data in Oil Reservoir Fields Tecnicas para
In recent years, 4D seismic data (4DS) have been used in the data assimilation process to improve numerical models that represent oil reservoirs. 4DS contains spatial information that provides insights into the reservoir changes caused by hydrocarbon production, such as movement of fluids (oil, gas, water) It helps engineers to calibrate reservoir simulation models and increase their capability of providing reliable production forecasts. However, in recent probabilistic approaches, it is common to have hundreds of numerical simulation model scenarios, which causes manual adjustment of each of those scenarios to be infeasible. In this vein, there is a need for automated methods that evaluate numerical models based on observed 4D seismic data. Comparing spatial information of seismic and numerical simulation data is a difficult task as usually, these data are converted to maps with different properties. Typical approaches are based on least square error, degenerating in a pixel wise comparison. However, these approaches suffer to deal with domain differences, scale differences, as well as differences in the lattice grid. Other methods seek to circumvent these difficulties using image analysis to measure 'visual' differences observed in those images. Nevertheless, most of these approaches use simple transformations and rely upon low level features, such as shapes. In this work, we propose three approaches to compare 4D seismic data and simulation models. Our methods focus on the image comparison and 'visual' differences, enabling a direct comparison of maps in the seismic domain with maps in the numerical simulation domain. We first propose an improvement to traditional methods using a statistical method to convert images of different physical properties into a common property, which can be used as an alternative to avoid forward and/or inversion procedures commonly used to bring 4D seismic and simulation data to a common magnitude. We also propose two novel approaches using Convolutional Neural Networks (CNNs) that learn spatial features through thousands of training examples. The CNNs are trained using examples composed of a reference image (4D seismic data) with two simulation model candidates. Additionally, we build an annotated dataset containing the simulation model images that best match the 4D seismic in different realistic scenarios while taking into account the specialists' knowledge. We use this test set to evaluate if a method is able to perform according to what specialists expect. Finally, we present an extensive comparison, qualitative and quantitative, of our approaches with other methods in the literature. The results show that our proposed methods are capable of more accurately identifying the best matched models, according to the specialists' answers.