Hydraulic properties of natural fractures are essential parameters for the modeling fluid flow and transport in subsurface fractured porous media. The cubic law, based on parallel-plate concept, has been traditionally used to estimate hydraulic individual fractures. This upscaling approach, however, is known overestimate properties. Dozens methods have proposed literature improve accuracy law. relative performance these various not well understood. In this work, a comprehensive review...

In this work, we develop a novel streamline (SL) simulation method that integrates seamlessly within the embedded discrete fracture model (EDFM). The SL-based is developed based on hybrid of two-point flux approximation (TPFA) and mimetic finite difference (MFD) methods, which applicable to two-phase anisotropic flow in fractured reservoirs. We refer approach as EDFM-TPFA-MFD-SL. operated an IMplicit Pressure Explicit Saturation (IMPES) manner. First, work establishes EDFM utilizing TPFA-MFD...

Accurate and efficient localization of CO2 leakage if occurred in subsurface formations, is significant importance achieving secure geological carbon sequestration (GCS) projects. However, this task inherently challenging due to the considerable uncertainties subsurface. In work, we develop a novel deep learning-assisted Bayesian framework for identifying potential sites based on reservoir pressure transient behavior measured at wellbores injection or observation wells. The method consists...

Summary History matching is a critical process used for calibrating simulation models and assessing subsurface uncertainties. This common technique aims to align the reservoir with observed data. However, achieving this goal often challenging due nonuniqueness of solution, underlying uncertainties, usually high computational cost simulations. The traditional approach based on trial error, which exhaustive labor-intensive. Some analytical numerical proxies combined Monte Carlo simulations are...

A full petrographic and petrophysical characterization of tight sandstones has been conducted as part ongoing study Carbon Dioxide Enhanced Oil Gas Recovery (CO 2 -EOR/EGR) CO sequestration.The main purpose this is to give novel perception into the interplay rock characteristics fluid flow in formations, which are candidates for EOR/EGR processes (macroscopic sweep vs. microscopic displacement efficiency).To achieve this, several experimental techniques, including routine core analysis,...

Abstract Geologic CO2 Sequestration (GCS) is a promising engineering technology to reduce global greenhouse emissions. Real-time forecasting of leakage rates an essential aspect large-scale GCS deployment. This work introduces data-driven, physics-featuring surrogate model based on deep-learning technique for rate forecasting. The workflow the development includes three steps: 1) Datasets Generation: We first identify uncertainty parameters that affect objective interests (i.e., rates). For...

Abstract Geological CO2 sequestration (GCS) has been a practical approach used to mitigate global climate change. Uncertainty and sensitivity analysis of storage capacity prediction are essential aspects for large-scale sequestration. This work presents rigorous machine learning-assisted workflow the uncertainty in deep saline aquifers. The proposed comprises three main steps: 1) dataset generation — we first identify parameters that impact aquifers then determine their corresponding ranges...

Abstract Geologic CO2 sequestration (GCS) has been considered a viable engineering measure to decrease global emissions. The real-time monitoring detect possible leakage is an important part of big-scale GCS deployment. In this work, we introduce deep-learning-based algorithm using hybrid neural network for detecting based on bottom-hole pressure measurements. proposed workflow includes the generation train-validation samples, coupling process training-validating, and model evaluation. This...

ABSTRACT We introduce a new hybrid numerical approach that integrates the Mimetic Finite Difference (MFD) and Discontinuous Galerkin (DG) methods, termed MFD‐DG method. This technique leverages MFD method to adeptly manage arbitrary quadrilateral meshes full permeability tensors, addressing flow equation for both edge‐center cell‐center pressures. It also provides an approximation phase fluxes across interfaces within cells. Subsequently, DG scheme, equipped with slope limiter, is applied...

Abstract The current study is focused on the effects of individual key ions (Ca2+, Mg2+, (SO4)2-) along with a few other common (Na+ and Cl-) carbonate rock by only injecting water controlled amount or combined into selected at reservoir temperature. A low field NMR technique has been tool choice for work since it allows monitoring physical chemical alteration surface after interacting fluids which contain specific types ions. In addition, non-destructive measurement, effect various...

Summary The extreme heterogeneity of carbonate in the form fracture corridors and superpermeability zones challenges efficient sweep oil both secondary- tertiary-recovery operations. In such reservoirs, conformance control is crucial to ensure injected water any enhanced-oil-recovery (EOR) chemicals optimally contact remaining with minimal throughput. Gel-based has been successfully applied on sandstone reservoirs. Achieving effective deep high-temperature however, remains a challenge...

Abstract History matching is critical in subsurface flow modeling. It to align the reservoir model with measured data. However, it remains challenging since solution not unique and implementation expensive. The traditional approach relies on trial error, which are exhaustive labor-intensive. In this study, we propose a new workflow utilizing Bayesian Markov Chain Monte Carlo (MCMC) automatically accurately perform history matching. We deliver four novelties within workflow: 1) use of...

Drilling is a requisite operation for many industries to reach targeted subsurface zone. During operations, various issues and challenges are encountered, particularly drilling fluid loss. Loss of circulation common problem that often causes interruptions the process reduction in efficiency. Such incidents usually occur when drilled wellbore encounters high permeable formation such as faults or fractures, leading total partial leakage fluids. In this work, semi-analytical solution mud...

Abstract Due to the scarcity and vulnerability of physical rock samples, digital reconstruction plays an important role in numerical study reservoir properties fluid flow behaviors. With rapid development deep learning technologies, generative adversarial networks (GANs) have become a promising alternative reconstruct complex pore structures. Numerous GAN models been applied this field, but many them suffer unstable training issue. In study, we apply Wasserstin with gradient penalty, also...

Abstract Utilization of neural networks to solve physical problems has been receiving wide attention recently. These are commonly named physics-informed network (PINN), in which the physics employed through governing partial differential equations (PDEs). Traditional PINNs suffer from unstable performance when dealing with flow highly heterogeneous domains. This work presents applicability extended PINN (XPINN) method solving problems. XPINN can create a full solution model PDEs by training...

Abstract The permeability of fractures, including natural and hydraulic, are essential parameters for the modeling fluid flow in conventional unconventional fractured reservoirs. However, traditional analytical cubic law (CL-based) models used to estimate fracture show unsatisfactory performance when dealing with different dynamic complexities fractures. This work presents a data-driven, physics-included model based on machine learning as an alternative methods. workflow development...

Abstract Detailed geological description of fractured reservoirs is typically characterized by the discrete-fracture model (DFM), in which rock matrix and fractures are explicitly represented form unstructured grids. Its high computation cost makes it infeasible for field-scale applications. Traditional flow-based static-based methods used to upscale detailed DFM reservoir simulation suffer from, some extent, low accuracy, respectively. In this paper, we present a novel deep learning-based...

Abstract The X-ray micro-Computed Tomography (μ-CT) is the primary tool for digital rock imaging, which provides foundation numerically studying petrophysical properties of reservoir rocks at pore scale. However, finite resolution μ-CT imaging cannot capture micro-porosity sub-micrometer scale in carbonate rocks. tradeoff between and field view (FOV) a persisting challenge industry. machine-learning-based single-image super-resolution techniques has rapidly developed past few years. It...

Abstract The huge resources of unconventional gas worldwide along with the increasing oil demand make contribution to be critical world economy. However, major challenges that operators face produce from are due need for identifying a sweetspot, commercial stimulation technique creates sufficient stimulated reservoir volume, very low permeability and different damage mechanisms such as water blocking, filtrate invasion during drilling, completion, or production. This paper describes new...

Abstract Carbonate's extreme heterogeneity in the form of fracture corridors and super-permeability thief zones challenges efficient sweep oil both secondary tertiary recovery operations. In such reservoirs, conformance control is crucial to ensure injected water any EOR chemicals optimally contact remaining with minimal throughput. Gel-based has been successfully applied on sandstone carbonate reservoirs. Achieving effective in-depth high-temperature however, remains a challenge due severe...