Improving 4D Seismic History Matching Through Data Analysis: A Localized Sensitivity Analysis Workflow

Abstract

Summary 4D seismic history matching (4D SHM) uses 4D seismic data to calibrate reservoir models to reduce production forecast uncertainty and improve reservoir surveillance. 4D seismic becomes very valuable in field developments with sparse well configurations and high areal uncertainty, such as offshore and carbon sequestration projects. Unlike the production data, the conventional uncertainty and sensitivity analysis (SA) with 4D seismic data might return misleading results. Due to the smooth nature of 4D seismic data, it is highly likely that low-frequency signals dampen the impact of model parameters on high-frequency signals. Consequently, some significant parameters are wrongly excluded from the 4D SHM process. Our work aims to address this issue by localizing the SA of 4D seismic data. The idea is first to identify specific seismic signals on the seismic maps and then perform the SA only at the individual locations rather than the entire map. This way we overcome the dampening effects of low-frequency signals. Several approaches to localize the SA are utilized. In one approach, we defined sliding windows to scan the seismic maps and then executed an SA inside the windows at each location. Other localization approaches employ dimensionality reduction and feature extraction tools. We used principal component analysis (PCA) and advanced machine learning methods such as autoencoders (AEs) and variational autoencoders (VAEs) to transform the 4D seismic maps into a latent space. The information content (the 4D seismic signals) in the high-dimensionality 4D seismic maps is represented by a few features in the latent space. Implementing an SA for each feature in the latent space is equivalent to performing SA with the seismic signals in the original map. The localized SA scheme is coupled with the ensemble smoother with multiple data assimilation (ESMDA) algorithm to carry out 4D SHM. Three 4D SHM scenarios were defined as full parameterization with no SA, conventional SA using the entire map, and localized SA. We ran these scenarios for a complex synthetic reservoir model based on a real field in the North Sea to match 4D P-wave seismic impedance. The results confirmed the superiority of the localized SA scenario which returned the final ensemble with the lowest error and the best match among the three scenarios. It also turned out that the PCA, for this specific case, is the most suitable methodology to localize the SA.