Data-driven surrogates for rapid simulation and optimization of WAG injection in fractured carbonate reservoirs

Conventional simulation of fractured carbonate reservoirs is computationally expensive because of the multiscale heterogeneities and fracture–matrix transfer mechanisms that must be taken into account using numerical transfer functions and/or detailed models with a large number of simulation grid cells. The computational requirement increases significantly when multiple simulation runs are required for sensitivity analysis, uncertainty quantification and optimization. This can be prohibitive, especially for giant carbonate reservoirs. Yet, sensitivity analysis, uncertainty quantification and optimization are particularly important to analyse, determine and rank the impact of geological and engineering parameters on the economics and sustainability of different Enhanced Oil Recovery (EOR) techniques. We use experimental design to set up multiple simulations of a high-resolution model of a Jurassic carbonate ramp, which is an analogue for the highly prolific reservoirs of the Arab D Formation in Qatar. We consider CO2 water-alternating-gas (WAG) injection, which is a successful EOR method for carbonate reservoirs. The simulations are employed as a basis for generating datadriven surrogate models using polynomial regression and polynomial chaos expansion. Furthermore, the surrogates are validated by comparing surrogate predictions with results from numerical simulation and estimating goodness-of-fit measures. In the current work, parameter uncertainties affecting WAG modelling in fractured carbonates are evaluated, including fracture network properties, wettability and fault transmissibility. The results enable us to adequately explore the parameter space, and to quantify and rank the interrelated effect of uncertain model parameters on CO2 WAG efficiency. The results highlight the first order impact of the fracture network properties and wettability on hydrocarbon recovery and CO2 utilization during WAG injection. In addition, the surrogate models enable us to calculate quick estimates of probabilistic uncertainty and to rapidly optimize WAG injection, while achieving significant computational speed-up compared with the conventional simulation framework

Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario

A variety of methods is available to quantify uncertainties arising with\-in the modeling of flow and transport in carbon dioxide storage, but there is a lack of thorough comparisons. Usually, raw data from such storage sites can hardly be described by theoretical statistical distributions since only very limited data is available. Hence, exact information on distribution shapes for all uncertain parameters is very rare in realistic applications. We discuss and compare four different methods tested for data-driven uncertainty quantification based on a benchmark scenario of carbon dioxide storage. In the benchmark, for which we provide data and code, carbon dioxide is injected into a saline aquifer modeled by the nonlinear capillarity-free fractional flow formulation for two incompressible fluid phases, namely carbon dioxide and brine. To cover different aspects of uncertainty quantification, we incorporate various sources of uncertainty such as uncertainty of boundary conditions, of conceptual model definitions and of material properties. We consider recent versions of the following non-intrusive and intrusive uncertainty quantification methods: arbitary polynomial chaos, spatially adaptive sparse grids, kernel-based greedy interpolation and hybrid stochastic Galerkin. The performance of each approach is demonstrated assessing expectation value and standard deviation of the carbon dioxide saturation against a reference statistic based on Monte Carlo sampling. We compare the convergence of all methods reporting on accuracy with respect to the number of model runs and resolution. Finally we offer suggestions about the methods' advantages and disadvantages that can guide the modeler for uncertainty quantification in carbon dioxide storage and beyond

Opportunities and challenges in CO2 geologic utilization and storage

CO2 geological utilization and storage is considered as an effective approach to deeply cut anthropogenic CO2 emissions. It is vital to enhance the amount of CO2 stored in the subsurface, at the same time to ensure safe and long-term subsurface storage of CO2 without any CO2 leakage. Science and engineering research in modeling concepts, experimental approaches, safety assurance and emerging CO2 geological utilization and storage technologies have driven the advancement of CO2 geological utilization and storage in recent years. In order to encourage communication and collaboration in CO2 geological utilization and storage research worldwide, a Sino-German joint symposium titled “Opportunities and Challenges in CO2 Geologic Utilization and Storage” was organized in Wuhan and Stuttgart from February 22 to 24, 2023, bringing together experts from China, Germany, and other countries. The symposium was jointly organized by Institute of Rock and Soil Mechanics, Chinese Academy of Sciences and Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart with financial support from the Sino-German Center for Research Promotion. A two-site hybrid meeting was held (participants in China met in Wuhan, participants in Germany met in Stuttgart, and other participants joined the meeting online), attracting more than 100 participants from around the world. The latest studies in the field of CO2 geological utilization and storage were presented at the symposium.Cited as: Zhang, L., Nowak, W., Oladyshkin, S., Wang, Y., Cai, J. Opportunities and challenges in CO2 geologic utilization and storage. Advances in Geo-Energy Research, 2022, 8(3): 141-145. https://doi.org/10.46690/ager.2023.06.0