History matching is critical in subsurface flow modeling. It is to align the reservoir model with the measured data. However, it remains challenging since the solution is not unique and the implementation is expensive. The traditional approach relies on trial and error, which are exhaustive and labor-intensive. In this study, we propose a new workflow utilizing Bayesian Markov Chain Monte Carlo (MCMC) to automatically and accurately perform history matching. We deliver four novelties within the workflow: 1) the use of multi-resolution low-fidelity models to guarantee high-quality matching, 2) updating the ranges of priors to assure convergence, 3) the use of Long-Short Term Memory (LSTM) network as a low-fidelity model to produce continuous time-response, and 4) the use of Bayesian optimization to obtain the optimum low-fidelity model for Bayesian MCMC runs. We utilize the first SPE comparative model as the physical and high-fidelity model. It is a gas injection into an oil reservoir case, which is the gravity-dominated process. The coarse low-fidelity model manages to provide updated priors that increase the precision of Bayesian MCMC. The Bayesian-optimized LSTM has successfully captured the physics in the high-fidelity model. The Bayesian-LSTM MCMC produces an accurate prediction with narrow uncertainties. The posterior prediction through the high-fidelity model ensures the robustness and precision of the workflow. This approach provides an efficient and high-quality history matching for subsurface flow modeling.
The current study is focused on the effects of the individual key ions (Ca 2+ , Mg 2+ , (SO 4 ) 2-) along with a few of other common ions (Na + and Cl -) on carbonate rock by only injecting water with controlled amount of individual or combined ions into the selected carbonate rock at reservoir temperature. A low field NMR technique has been a tool of choice for the current work since it allows monitoring the physical and chemical alteration of rock surface after interacting with fluids which contain specific types and amount of ions. In addition, since NMR is non-destructive measurement, the effect of various types of fluids with the identical rock sample before, during, and after core flooding test can be monitored repeatedly.NMR results indicate that the water-rock interaction changes when injecting different types of ions. The interaction a of key divalent key ions, Ca 2+ , Mg 2+ , and (SO 4 ) 2-, on the carbonate rock surface are observed with relatively weak reactivity of Ca 2+ compare to the other two. At the reservoir temperature, 90°C, the reactivity of Mg 2+ with the carbonate rock is greater than that of Ca 2+ . The reactivity of multivalent anion (SO 4 ) 2is also significant with carbonate rock surface, but it will induce surprising behavior on NMR response. The fundamental understandings acquired by the current study, effects of key ions in carbonate rock with single-phase fluid will be one of the key building blocks to develop rigid understanding of more complicated multi-phase fluid interaction with various types of reservoir rocks, and eventually draw conclusions on how these ions change rock wettability.
The application of conventional surfactant-based enhanced oil recovery (EOR) suffers from either the adsorption of the surfactant on the rock near the wellbore or from diffusion into small water-filled pores. An approach to mitigate the loss of surfactant and enhance deliverability to the oil phase is achieved by formulating the surfactant molecules into nano-sized particles, which are referred to as NanoSurfactant (NS) in this study. An essential step to unleash the potential of NS in EOR is the accurate understanding of its performance under conditions reasonably close to actual conditions. In the current study, crude oil, synthetic brine, and petroleum sulfonate based NS were used. Eight limestone core plugs with a permeability range of about 125 millidarcies (mD) to 1282 mD were utilized in this study. Coreflooding was conducted under the near-reservoir condition. Advanced low-field nuclear magnetic resonance (NMR) techniques have been used to quantify the oil saturations by both secondary water injection and tertiary NS injection. The NMR techniques have also been used to assess the mechanisms underlying oil mobilization in carbonate core plugs. The protons in brine and NS solution were substituted with proton (H1) NMR invisible deuterium to enhance the contrast between oil and brine. The core samples were selected to investigate the effect of the NS soaking condition (soaking versus non-soaking), the NS injection rate, and the remaining oil saturation on the observed oil mobilization, respectively. All core plugs were initially saturated with oil and water, waterflooded, injected with NS (with and without soaking), followed by chase waterflooding. NMR measurements were conducted after each flooding stage. The results of the current study demonstrate that the investigated NS has a great potential for EOR applications in carbonate reservoirs, and that soaking NS before chase waterflooding enhances its efficiency. Significant oil mobilization was observed when soaking was applied before chase waterflooding. The oil mobilization was much lower without soaking of NS for the cores with low quality and less oil wetting tendency. The analysis indicated that NS flooding can produce both of the trapped oil and oil adsorbed to the rock surfaces.
Detailed geological description of fractured reservoirs is typically characterized by the discrete-fracture model (DFM), in which the rock matrix and fractures are explicitly represented in the form of unstructured grids. Its high computation cost makes it infeasible for field-scale applications. Traditional flow-based and static-based methods used to upscale detailed geological DFM to reservoir simulation model suffer from, to some extent, high computation cost and low accuracy, respectively. In this paper, we present a novel deep learning-based upscaling method as an alternative to traditional methods. This work aims to build an image-to-value model based on convolutional neural network to model the nonlinear mapping between the high-resolution image of detailed DFM as input and the upscaled reservoir simulation model as output. The reservoir simulation model (herein refers to the dual-porosity model) includes the predicted fracture-fracture transmissibility linking two adjacent grid blocks and fracture-matrix transmissibility within each coarse block. The proposed upscaling workflow comprises the train-validation samples generation, convolutional neural network training-validating process, and model evaluation. We apply a two-point flux approximation (TPFA) scheme based on embedded discrete-fracture model (EDFM) to generate the datasets. We perform trial-error analysis on the coupling training-validating process to update the ratio of train-validation samples, optimize the learning rate and the network architecture. This process is applied until the trained model obtains an accuracy above 90 % for both train-validation samples. We then demonstrate its performance with the two-phase reference solutions obtained from the fine model in terms of water saturation profile and oil recovery versus PVI. Results show that the DL-based approach provides a good match with the reference solutions for both water saturation distribution and oil recovery curve. This work manifests the value of the DL-based method for the upscaling of detailed DFM to the dual-porosity model and can be extended to construct generalized dual-porosity, dual-permeability models or include more complex physics, such as capillary and gravity effects.
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