Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)

Carbon dioxide sequestration in geological formations has been proposed as a promising solution to reach net zero carbon emissions but the success of underground CO2 storage in sandstone formations depends on the brine/CO2 wettability of sandstone. Research evidence showed that natural geological formation is hydrophobic even in the presence of minute concentration of inherent organic acids. This study investigates the effect of methylene blue (MB) on CO2 wettability of organic-acid contaminated quartz through the tilted plate contact angle measurement method. Pure quartz substrates were aged in a stearic acid/n-decane solution for one week and subsequently modified with different concentrations of MB (ranging from 10 to 100 mg/L) at a temperature of 60 °C. Advancing (θa) and receding (θr) contact angles were measured under varying conditions of temperature (25 °C and 50 °C), pressure (ranging from 10 to 20 MPa), and salinity (0–0.3 M). The experimental results indicate that pure quartz, when aged in a stearic acid solution, becomes CO2-wet at all temperature, pressure, and salinity conditions. However, at any physio-thermal condition, the wettability of the quartz surfaces was reversed when treated with different concentrations of MB, transitioning to a water-wet state. The findings of this research demonstrate the potential of MB to modify the wetting behaviour of quartz surfaces and enhance CO2 residual trapping in sandstone formations.

We review published literature of induced seismicity resulting from CO2 sequestration in geological formations. We explain why these processes are related and explain various hazard levels associated with this practice. We also discuss why the depth of induced seismicity is a crucial parameter when evaluating risks for caprock failure and triggered (felt) earthquakes in the basement as well as characterise the reservoir (i.e., formation used for sequestration). Several case studies observe induced seismicity, mostly associated with basement activated faults. Only one case of induced seismicity suggests seal failure resulting from CO2 sequestration. However, our review also documents an apparent lack of adequate monitoring arrays installed to capture induced seismicity. We found a large number of case studies where no induced seismicity has been detected – most likely due to the seismic monitoring network physical limitations. Despite the limited number of reported cases, we find a weak positive correlation between seismic magnitude and the volume of injected CO2 . Furthermore, and perhaps more importantly for microseismic monitoring network design, we show strong evidence that long-term CO2 sequestration leads to induced seismicity at distances exceeding kilometres from the injection well. We discuss this observation is similar to salt-water disposal induced seismicity

In Geological Carbon Sequestration (GCS), effectively managing the project requires predicting state variables such as pressure and saturation. However, numerical simulation of multiphase flow in subsurface porous media involves solving large linear algebra systems, resulting in a substantial computational burden. This can make it impractical for real-time history matching or optimization. Meanwhile, deep learning-based surrogate models are emerging as fast and accurate approximators. This study delves into the application of the Encoder-Decoder Convolutional Long Short-Term Memory (ED-ConvLSTM) neural network for predicting the complex evolution of state variables under dynamic CO2 injection schemes. Inception blocks enhanced with light-weighted attention modules are introduced in the encoder to extract high-dimensional input features. ConvLSTM is employed to propagate spatial temporal information in the low-dimensional latent space. Further, progressive upsampling blocks are used to reconstruct the latent features for the desired output. Instead of taking discrete time steps as an input feature, the proposed network captures the dynamic dependencies with the inherent ConvLSTM cell. The network has access to data at only portion of the initial time steps during training stage, while it is used to predict the state variables at unseen time steps during testing stage. Results show that the network can produce excellent predictions for both pressure and saturation, even at unseen future time steps. The remarkable generalizability to different geological permeability fields is also evaluated. ED-ConvLSTM outperforms the standard U-Net by far, especially when predicting beyond the training time period. These numerical experiments demonstrate the advantages of ED-ConvLSTM in terms of prediction accuracy, extrapolability and generalizability. This study highlights the importance of incorporating recurrent connections into the deep neural networks for simulating time-dependent multiphase flow problems. The proposed methodology has great potential in GCS surrogate modeling and offers a possible approach for real-time optimization of CO2 injection.

Multi-channel analysis of surface waves is a seismic method employed to obtain useful information about shear-wave velocities in the near surface. A fundamental step in this methodology is the extraction of dispersion curves from dispersion spectra, with the latter usually obtained by applying specific processing algorithms onto the recorded shot gathers. Although the extraction process can be automated to some extent, it usually requires extensive quality control, which can be arduous for large datasets. We present a novel approach that leverages deep learning to identify a direct mapping between seismic shot gathers and their associated dispersion curves (both fundamental and first higher order modes), therefore by-passing the need to compute dispersion spectra. Given a site of interest, a set of 1D compressional and shear velocities and density models are created using prior knowledge of the local geology; pairs of seismic shot gathers and Rayleigh-wave phase dispersion curves are then numerically modelled and used to train a simplified residual network. The proposed approach is shown to achieve high-quality predictions of dispersion curves on a synthetic test dataset and is, ultimately, successfully deployed on a field dataset. Various uncertainty quantification and convolutional neural network visualization techniques are also presented to assess the quality of the inference process and better understand the underlying learning process of the network. The predicted dispersion curves are inverted for both the synthetic and field data; in the latter case, the resulting shear-wave velocity model is plausible and consistent with prior geological knowledge of the area. Finally, a comparison between the manually picked fundamental modes with the predictions from our model allows for a benchmark of the performance of the proposed workflow.

Geomechanics has a marked impact on the safe and sustainable use of the subsurface. This Special Publication contains contributions detailing the latest efforts in present-day in-situ stress characterization, prediction and modelling from the borehole to plate-tectonic scale. A particular emphasis is on the uncertainties that are often associated with geomechanics.