查看更多>>摘要：The current industrial practice calls for wells to be hydraulically fractured in as many as 50 stages, depending on the formation. The integrity of the cemented wellbore sections around the perforations has been a major concern in most hydraulically fractured wells. In this paper, we have conducted a comprehensive numerical modelling study to understand the possible impact of hydraulic fracturing operation on the integrity of the cemented wellbore sections, especially around the perforations where the fracture fluid is injected. A numerical model allowing full coupling of solid/liquid flow has been adopted to simulate the fracturing fluid injection and the resultant deformation of cemented casing sections around the perforations. Field data from a hydraulically fractured deep shale gas well in China were used to demonstrate the impact of the operational variables on the integrity of the cemented wellbore section. We also studied the influence of fracture fluid injection pressure, mechanical properties of the cement, casing and the reservoir, perforation azimuth, and fracture fluid viscosity on the cement sheath failure. Simulation results have shown that the cement sheath failure at the cement-casing interface may occur in the form of debonding in the radial direction. The cement debonding occurring at the casing-cement interface due to high frac fluid injection pressure, would be one of the main reasons for the poor hydraulic fracturing job performance commonly observed in shale gas wells. Following conclusions can also be drawn from simulation results: 1) Injection pressure has an important impact on the cement debonding. For the specific reservoir conditions investigated in this paper, to avoid cement debonding, the injection pressure should be no more than 75 MPa;; 2) Use of a casing with a larger elasticity modulus and cement with lower elasticity modulus and lower permeability are conducive for improved cement bonding quality;; 3) For a shale gas reservoir with lower permeability, these requirements for the casing and cement should be even stricter.
Jessica I. Lozano-NavarroArturo Palacio-PerezEdgardo J. Suarez-Dommguez
12页
查看更多>>摘要：The transport of extra-heavy crude oil is a severe problem in the oil industry due to its viscosity, asphaltenes content, and tendency to precipitate. These crude oil's characteristics cause clogging of the pipes and increase production costs. One of the solutions for this problem is the addition of compounds that reduce the viscosity of crude oil and the self-aggregation of asphaltenes. In recent years, new 'green' additives have been investigated to replace the use of traditional and more toxic substances, such as toluene. In this study, new common geranium (Pelargonium hortorum) aqueous extracts were synthesized with 1, 5, and 10% (w/v). Its effect on the Mexican extra-heavy (8 °API) crude oil blend was analyzed in a shear rate range of 0.1-60 1/s and constant temperatures of 30, 40, and 50 °C. In addition, its pH, TDS, and EC were measured on days 1 and 15 after synthesis. These synthesized compounds were qualitatively determined using phytochemical analysis. The possible relationship between these compounds and the viscosity modification was analyzed. The results suggest that using these compounds as viscosity modifiers for extra-heavy crude oil could be promising, at higher concentrations in the range of 5-10% and for a temperature range of 40-50 °C, and for lower shear rates. The conclusions suggest further investigation of the common geranium aqueous extracts to understand its effect on crude oil properties.
查看更多>>摘要：Numerical simulation of oil reservoirs is one of the most commonly used methods for reservoir production prediction, but its accuracy is based on accurate geological modeling and high-quality history matching. Therefore, numerical simulation is time-consuming and costly and requires extensive information. Traditional back-propagation neural networks and their improved algorithms are widely used for production prediction, but they are not suitable for time-series prediction problems. Based on variations in oil production, this study proposes a reservoir production prediction model based on a combined convolutional neural network (CNN) and a long short-term memory (LSTM) neural network model optimized by the particle swarm optimization (PSO) algorithm. First, the model extracts important temporal data features through the upper CNN, which is next imported to the lower LSTM network to further extract correlation features in the time dimension;; then, it iteratively optimizes the key hyperparameters in the CNN-LSTM model through the PSO algorithm;; finally, it uses the trained model for reservoir prediction. Compared with the training results of the LSTM neural network and CNN model, the PSO-CNN-LSTM model has higher prediction accuracy in time-series production prediction. Our proposed hybrid model is a data-driven method and is based on routinely available production data. Quick and accurate production prediction can lead to better informed operational decisions and optimization of recovery and economics.
查看更多>>摘要：Relative permeability of surfactant-polymer (SP) flooding is determined by an inversion method based on numerical simulation and unsteady-state coreflooding experiment. Compared with the common methods, this method is more time-saving and simpler than steady-state method, and considers more physicochemical properties (adsorption and diffusion) of SP than unsteady-state method based on Buckley-Leverett theory. It is also more applicable to reservoir engineering than pore-scale simulation. After the reliability is confirmed, the superiority of the method is strongly proved by studying the influence of adsorption and diffusion of SP on relative permeability. Comparing relative permeability with and without effect of adsorption and diffusion of SP, the findings show adsorption and diffusion of SP significantly reduce the average inversion error of relative permeability curve from 11.9 to 3.7 for oil and from 10.7 to 1.8 for water. On this basis, the variations of SP-flooding relative permeability under the effect of polymer and surfactant are discussed in turn. In the existing researches, the quantitative characterization of variation of SP-flooding relative permeability curves is limited by curve crossing and endpoint saturation changes. In this work, normalized relative permeability is introduced to eliminate the adverse effect caused by curve crossing and endpoint saturation changes. The findings show that although the rise of aqueous-phase viscosity reduces water relative permeability, it has little effect on normalized water relative permeability. Normalized oil and water relative permeability increase in logistic functions with the decrease of IFT in semi-logarithmic plot. Water relative permeability at residual oil endpoint decreases linearly as aqueous-phase viscosity increases, and increases exponentially as IFT decreases in semi-logarithmic plot. Ultimately, the functional models based on normalized relative permeability and cubic B-spline are built through multiple regression, which can calculate relative permeability of SP flooding in term of IFT and aqueous-phase viscosity.
Paulo Henrique RanazziXiaodong LuoMarcio Augusto Sampaio
25页
查看更多>>摘要：In present days, Iterative ensemble smoothers (IES) are among the main methods to perform ensemble-based history matching in petroleum reservoirs. Generally, some localization technique is applied to the IES to prevent ensemble collapse, which is the consequence of an excessive reduction of the posterior ensemble variance. When the standard distance-based localization is applied, the assimilation of non-local parameters is difficult, and besides that, this kind of methodology has also several intrinsic parameters that need to be defined before the assimilation. In contrast, adaptive localization methods aim to overcome the noticed problems of distance-based localization, by using some statistical method to define the localization. This article proposes a novel adaptive localization scheme, on top of two preexisting techniques: pseudo-optimal and correlation-based localizations. The motivation here is to further improve the adaptive localization scheme, by combining the strengths of these two preexisting techniques. The efficacy of the proposed localization scheme is tested in one 2D and one 3D case studies, whereas the latter case study involves a field-scale reservoir model with both local and non-local parameters, which often impose challenges on the conventional localization schemes. In comparison to other evaluated localization schemes, our results indicate that the proposed adaptive localization scheme achieves improved history matching performance.
查看更多>>摘要：Gas hydrates are ice-like crystalline solids that form in marine sediments or permafrost layers. The inhomogeneous distribution of the hydrate will form different hydrate patterns such as grain-coating and pore-filling hydrate, which will further cause permeability anisotropy in different directions. In this study, X-ray computed tomography (CT) test and image analysis by AVIZO were used to investigate the permeability anisotropy of hydrate-bearing sands. The image analysis revealed the following results: (1) For both hydrate patterns, the hydrate formation induced the largest permeability decrease in the y direction and the smallest one in the z direction. (2) The degree of anisotropy of pores after the pore-filling hydrate formation is larger than that after the grain-coating hydrate formation. A decrease in the pore anisotropy ratio will induce a significant increase in permeability anisotropy. (3) Although the increase in the pore shape factor ratio after the pore-filling hydrate formation is smaller than that after the grain-coating hydrate formation, the permeability anisotropy increase is much larger. (4) The tortuosity increase is the largest in the y direction and the smallest in the 2 direction, which is consistent with the permeability variations. Pore-filling hydrate formation induces a larger variation in the tortuosity ratio, which causes a higher permeability anisotropy.
查看更多>>摘要：The viscosity of heavy oil is of great importance on crude oil recovery, and the intrinsic relationship between the molecular structure of heavy oil and its viscosity is still unclear. To this end, the chemical structure of heavy oil is characterized by Scanning Electron Microscopy (SEM) and Diffusion-ordered Spectroscopy (DOSY). The relationship between the molecular structure and viscosity of heavy oil is revealed. The morphology of molecular aggregates is dominated by spherical particles, vesicles and films, and the corresponding viscosity increases sequentially. DOSY show that the heavy oil molecules rapidly gathered into aggregates with increasing concentrations. If the saturated aggregation concentration (maximum degree of aggregation) is exceeded, the volume of the single aggregate remains generally fixed but its quantity begins to increase. In addition, the structure-activity relationship between chemical agents and emulsification-stripping, including their mechanism of effect are systematically studied by interfacial tension (IFT), emulsification and micromodel experiments. The results show that the hydrophilic-lipophilic balance (HLB) values of surfactants that stabilize the emulsions are between 13.7 and 14.5. There is no obvious consistency between the emulsification effect of the surfactant and its IFT, and the stripping effect depends on its permeation and wetting action on the solid interface. The formation of emulsions in porous media mainly depends on the rotational disturbance and shear fracture of the displacing fluid, and the wettability of the solid surface determines the peeling rate of the oil film. Comparing with the blockage of large-sized emulsions, the migration of small-sized droplets in the channels as a continuous phase contributes more to the enhanced oil recovery.
查看更多>>摘要：Foamy-oil flow is one of the main mechanisms for cold heavy oil production with sand (CHOPS) and cyclic solvent injection (CSI) processes. The flow pattern of foamy oil was observed in previous experimental studies under reasonable reservoir conditions. This study aims to investigate the foamy-oil flow mechanisms through theoretical modeling. A mathematical model is developed, which couples a solvent concentration field and a pressure field through flow velocity and oil viscosity, in which a pseudo-chemical reaction is used to describe the release of gas. The model is numerically solved by the Newton-Raphson iterative method. Results show that, firstly, during the pressure drawdown process, propane concentration decreases and oil viscosity increases with solvent exsolution in the transition zone, which restricts the bubbles coalescence and conduces to the formation of foamy oil. Secondly, the existence of bubbles leads to the buildup of local pressure gradient and causes the spatial difference of flow velocity. Moreover, when the threshold velocity is reached, the bubbles coalesce rapidly to form free gas, pushing the oil in the foamy region forward to the gas zone. Finally, the total waves of foamy-oil flow are generally 2 to 4, depending on the initial solvent concentration, oil viscosity, and pressure depletion rate. Higher initial solvent concentration, faster pressure depletion, and lighter original heavy oil lead to the easier induction of foamy-oil flow.
Robson P. Barboza JuniorMateus P. SchwalbertJovani L Favero
13页
查看更多>>摘要：In carbonate reservoir treatment, uneven distribution of acid may occur due to strong heterogeneity. To avoid the selective flow of fluid into the high permeability zones, viscoelastic surfactant-based self-diverting acids are commonly used in stimulation operations. To better comprehend this phenomenon, this work proposes a mathematical model to describe the dissolution process associated with this problem. The rheology of the acid solution was experimentally obtained for some degrees of neutralization and the flow curves were fitted by a Carreau model with neutralization dependent coefficients. In addition to the usual governing equations of this problem that include mass conservation, momentum balance, and acid concentration conservation, we considered a transport equation for the reaction products. The solid-liquid interaction term at the porous scale is determined by the fractions of acid and products, that in addition to brine compose the total fluid phase. Simulations in plug geometry were performed to validate this model against experimental results in different samples, where good agreement was found.
查看更多>>摘要：Three-dimensional (3D) X-ray micro-computed tomography (|iCT) has been widely used in petroleum engineering because it can provide detailed pore structural information for a reservoir rock, which can be imported into a pore-scale numerical model to simulate the transport and distribution of multiple fluids in the pore space. The partial volume blurring (PVB) problem is a major challenge in segmenting raw μCT images of rock samples, which impacts boundaries and small targets near the resolution limit. We developed a deep-learning (DL)-based workflow for accurate and fast partial volume segmentation. The DL model's performance depends primarily on the training data quality and model architecture. This study employed the entropy-based-masking indicator kriging (IK-EBM) to segment 3D Berea sandstone images as training datasets. The comparison between IK-EBM and manual segmentation using a 3D synthetic sphere pack, which had a known ground truth, showed that IK-EBM had higher accuracy on partial volume segmentation. We then trained and tested the UNet++ model, a state-of-the-art supervised encoder-decoder model, for binary (i.e., void and solid) and four-class segmentation. We compared the UNet++ with the commonly used U-Net and wide U-Net models and showed that the UNet++ had the best performance in terms of pixel-wise and physics-based evaluation metrics. Specifically, boundary-scaled accuracy demonstrated that the UNet++ architecture outperformed the regular U-Net architecture in the segmentation of pixels near boundaries and small targets, which were subjected to the PVB effect. Feature map visualization illustrated that the UNet++ bridged the semantic gaps between the feature maps extracted at different depths of the network, thereby enabling faster convergence and more accurate extraction of fine-scale features. The developed workflow significantly enhances the performance of supervised encoder-decoder models in partial volume segmentation, which has extensive applications in fundamental studies of subsurface energy, water, and environmental systems.
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