SPE Journal最新文献

The increasingly severe wellbore instability problem and the intensified environmental requirements necessitate the development of high-performance and environmentally friendly plugging materials for drilling fluid. In this work, a novel core-shell nanocomposite (PDSA) with nano-silica (nano-SiO₂) as the rigid core and hydrophobic resin derived from dehydroabietic acid (DHAA) of pine rosin and crosslinked hydrophilic layer of 2-acrylamido-2-methyl-1-propanesulfonic acid and N, N-dimethylacrylamide (DMA) as the polymer shell was synthesized through semi-continuous emulsion polymerization. The molecular structure of PDSA was confirmed by proton nuclear magnetic resonance and Fourier transform infrared spectra analysis. Particle-size distribution and morphology measured by dynamic light scattering, scanning electron microscopy, and transmission electron microscopy revealed that PDSA was a monodisperse nanosphere with a particle size of around 98 nm, with a core-shell structure and possessed excellent long-term colloidal dispersion stability. The nano-microporous plugging performance of PDSA was evaluated using tight sandstone cores, shale cuttings, and filter membranes (200–400 nm) as plugging media. The results showed that PDSA could form effective aggregated plugging zones in nano-micropores and fractures in sandstone core and shale samples, lowering the core permeability by 78% and improving the shale recovery to above 80%, superior to conventional plugging agents of nano-polyester (NP) and sulfonated asphalt. PDSA also effectively minimized the nano-micropore fluid loss for filter membranes under high-temperature and high-pressure (HTHP) conditions. Furthermore, based on the response surface methodology (RSM) design, the established statistical significant prediction model for HTHP nano-microporous fluid loss indicated the main controlling factor of temperature and its interactive effects with PDSA dosage and membrane size. The high-temperature-induced deformation of PDSA in conjunction with the rigid core was conducive to enhancing and maintaining the HTHP plugging effect within 180°C. The plugging mechanism of PDSA was revealed to be the core-shell synergistic plugging effects of the interparticle bridging and gap filling of the rigid core and the adhesive film forming of the rosin resin shell. The study might provide a novel strategy for preparing high-performance and eco-friendly nano-plugging agents from natural rosin to maintain wellbore stability and relieve environmental pressure, especially for applications in the deep shale and tight formations associated with high-temperature and nano-microporous harsh conditions and the environmentally sensitive areas.

Assimilation of time-lapse (4D) seismic data with ensemble-based methods is challenging because of the massive number of data points. This situation requires excessive computational time and memory usage during the model updating step. We addressed this problem using a deep convolutional autoencoder to extract the relevant features of 4D images and generate a reduced representation of the data. The architecture of the autoencoder is based on the VGG-19 network, a deep convolutional architecture with 19 layers well-known for its effectiveness in image classification and object recognition. Some advantages of VGG-19 are the possibility of using some pretrained convolutional layers to create a feature extractor and taking advantage of the transfer learning technique to address other related problem domains. Using a pretrained model bypasses the need for large training data sets and avoids the high computational demand to train a deep network. For further improvements in the reconstruction of the seismic images, we apply a fine-tuning of the weights of the latent convolutional layer. We propose to use a fully convolutional architecture, which allows the application of distance-based localization during data assimilation with the ensemble smoother with multiple data assimilation (ES-MDA). The performance of the proposed method is investigated in a synthetic benchmark problem with realistic settings. We evaluate the methodology with three variants of the autoencoder, each one with a different level of data reduction. The experiments indicate that it is possible to use latent representations with major data reductions without impairing the quality of the data assimilation. Additionally, we compare central processing unit (CPU) and graphics processing unit (GPU) implementations of the ES-MDA update step and show in another synthetic problem that the reduction in the number of data points obtained with the application of the deep autoencoder may provide a substantial improvement in the overall computation cost of the data assimilation for large reservoir models.

This study focuses on carbon capture, utilization, and sequestration (CCUS) via the means of nonlinearly constrained production optimization workflow for a CO₂-enhanced oil recovery (EOR) process, in which both the net present value (NPV) and the net present carbon tax credits (NPCTC) are bi-objectively maximized, with the emphasis on the consideration of injection bottomhole pressure (IBHP) constraints on the injectors, in addition to field liquid production rate (FLPR) and field water production rate (FWPR), to ensure the integrity of the formation and to prevent any potential damage during the life cycle injection/production process. The main optimization framework used in this work is a lexicographic method based on the line-search sequential quadratic programming (LS-SQP) coupled with stochastic simplex approximate gradients (StoSAG). We demonstrate the performance of the optimization algorithm and results in a field-scale realistic problem, simulated using a commercial compositional reservoir simulator. Results show that the workflow can solve the single-objective and bi-objective optimization problems computationally efficiently and effectively, especially in handling and honoring nonlinear state constraints imposed onto the problem. Various numerical settings have been experimented with to estimate the Pareto front for the bi-objective optimization problem, showing the trade-off between the two objectives of NPV and NPCTC. We also perform a single-objective optimization on the total life cycle cash flow, which is the aggregated quantity of NPV and NPCTC, and quantify the results to further emphasize the necessity of performing bi-objective production optimization, especially when used in conjunction with commercial flow simulators that lack the capability of computing adjoint-based gradients.