查看更多>>摘要：Gypsum-salt rock is typically developed in carbonate reservoirs, and this rock has both constructive and destructive effects on the reservoir. The ways in which gypsum rock controls reservoir development are closely related to the diagenetic conditions. In this study, a typical saline lacustrine basin, the Qaidam Basin in China, was selected to examine the influence of gypsum-salt rock on the development of the carbonate reservoir under different diagenetic conditions. Four main geological factors were assessed: formation condition (temperature), typical salt mineral (anhydrite), and fluid properties (Ca2+ and Mg2+), along with multi-group fluid-rock chemical reaction models devised using multiphase-flow solute-transport simulation technology. Mineral dissolution, precipitation, and transformation in the reservoir under various temperature, pressure, fluid, and mineral conditions were analyzed, and the change of reservoir porosity was calculated. The results showed that the concentration of Ca2+ in fluid controls the dissolution and precipitation of carbonate minerals in the reservoir; moreover, continuous and sufficient Mg2+ is a necessary condition for dolomitization. Precipitation of anhydrite decreases with increase of temperature, verifying that anhydrite precipitates more easily at low temperature. Dissolution of calcium-containing minerals in overlying gypsum-salt rock can lead to mineral precipitation in the subjacent reservoir and reduce its quality.
查看更多>>摘要：The initial productivity of directional oil wells is essential to research during the early development stage of offshore oilfields. Since the influence factors of productivity are numerous, the nonlinear relationship among them and productivity is hard to accurately describe by the physical models in practical application. The data-driven model provides an alternative way to deal with this problem, although it neglects the physical correlation between productivity and its influence factors. Therefore, for combining the advantage of the physical model and data-driven model, the reservoir engineering theory was used to constrain the data-driven method in this study. Based on the eXtreme gradient boosting (XGBoost) trees algorithm, this paper proposed a novel physical constrained data-driven model to predict the initial productivity. The reservoir engineering theory, including the productivity formula and the monotonic correlation between productivity and the input feature, was employed as the physical constraints in this model. Moreover, the Spearman Correlation Coefficient and a modified Recursive Feature Elimination were combined to develop a feature selection method for quantitatively selecting the main influence factors of the initial productivity. Based on the production and geological data from 87 wells, the permeability, completed thickness, oil viscosity, clay content, correct factor for interference, choke size, and drawdown were selected from 19 features as the main influence factors of the initial productivity. The prediction accuracy of the model proposed in this paper is 80.18%, which is better than the previous data-driven models. Furthermore, the physical constraints have been proved to improve the forecasting accuracy of the data-driven method by 10.09%.
Jo, SuryeomAhn, SeonginPark, ChanghyupKim, Jaejun...
20页
查看更多>>摘要：This paper presents a new deep-learning-based generative method applicable to history matching without an inverse scheme. Multiple-point geostatistics is used to construct a prior population stochastically. A convolutional variational autoencoder (VAE) with probabilistic latent space is trained as the generative method, and kmeans clustering, nondominated sorting, and multilevel geomodel generations are performed based on flow responses. The applicability of the developed workflow was confirmed using a waterflooding problem with multiple wells in fluvial channel reservoirs. The VAE generates new geomodels based on the latent features and builds equiprobable models neighboring the representative models that reflect the observed production performance. The geomodels match the oil production profiles reliably as the steps progress and accurately forecast the water breakthrough time and liquid production trajectories. The density map of plausible geomodels explains reasonably the uncertainty of channel connectivity. The structural similarity index confirms that the generated geomodels become similar to the target reservoir and thus that the developed VAE-based framework creates geomodels that preserve geological realism. This proposed method involves relatively less time-consuming simulations without any inverse or optimization processes; nonetheless, it generates plausible geomodels in dimensionality-reduced latent space. The study methods and findings are thus applicable to scale-variant data integration and uncertainty assessment.
查看更多>>摘要：Nanoparticles are of interest in recent oil production process due to their potential to wettability alteration, but not interfacially active at the crude oil-water interface. Stability loss in brine environment, where nanoparticles tend to aggregate, is another issue for field implementation. Hence, recent challenge is to functionalize nano particles that are interfacially active and still stabilized in brine. The current study fabricated and characterized the polyvinylpyrrolidone-coated silica composite nanoparticles for their interfacial activity at the crude oil-water interface. Reduction in oil-water interfacial tension was observed and more dramatic with increasing particle concentration, confirming particle adsorption performance. In low-salinity brine (2000 ppm NaCl), the composite particles remained stabilized with weakened electrostatic force between particle and crude oil surfaces, while their size was smaller due to polymer shell dehydration. These led to faster diffusion rate than in Milli-Q water, which affected the rate of change in oil-water/brine interfacial tension, with the early-stage adsorption being a diffusion-controlled in both fluids. At equivalent particle concentration, the oil-water interfacial tensions in brine were lower than those of Milli-Q water (by similar to 2 mN/m), with interfacial coverage of the particles at the interface was found to be higher in the brine. Such difference is attributed to a weaker repulsive force between particle and the interface, induced by surface charge screening that is only present in brine. The study has demonstrated the potential use of polymer-coated nanoparticles as suitable additives for use in oil recovery, which can be used concurrently with low-salinity brine as a combined fluid. While both chemicals are known to construct disjoining pressure for wettability alteration, advantage of using interfacially active nanoparticles is additional mechanism to enhance oil recovery, i.e. reducing the oil-water interfacial tension, which unfunctionalized particles could not contribute.
查看更多>>摘要：Mono ethylene glycol (MEG) is highly utilized during gas production to mitigate hydrate formation issue. However, exposing the MEG to reboiler higher temperatures during distillation will lead to the reduced hydrate inhibition performance of the MEG due to thermal degradation and accumulation of organic acids such as glycolic, acetic and formic acids. The hydrate inhibition performance of the thermally degraded MEG was measured isobarically for pure methane gas using a cryogenic sapphire cell for the pressure ranges of 5500 to 20500 kPa. The objective is to determine and optimize the thermodynamic hydrate inhibition performance of thermally degraded MEG influenced by reboiler operation and MEG concentration in the lean MEG product. This research determines that operating the reboiler at high MEG concentration of 80.8 vol % for longer durations of 6.0 h raises the dissociation temperature by an average of 1.4 degrees C compared to 0.9 degrees C for the lower MEG concentration of 40.2 vol % for similar duration of 6.0 h. This suggests that lower MEG concentration will yield lower dissociation temperatures at the same retention time. Hence, the MEG thermodynamic inhibition performance is optimal at lower MEG concentration which results in lower dissociation temperature.
查看更多>>摘要：The Qaidam Basin has experienced multiple tectonic movements since the Carboniferous. Investigating the formation, adjustment, and alteration of oil and gas reservoirs is helpful to optimize exploration targets. Oil and gas charging, accumulation, and adjustment in the Carboniferous Keluke Formation in the eastern Qaidam Basin were comprehensively analyzed through integrating fluid inclusion petrographic observations, homogenization temperature and salinity determination, basin modeling, bitumen geochemical analysis, and balanced crosssection restoration. Fluid inclusions and basin modeling show an early single-stage and continuous hydrocarbon generation in the Keluke Formation. The C(2)k(2) source rocks became marginally mature, mature, and highly mature at 312 Ma, 298 Ma, and 248 Ma, resulting in oil generation at the marginally maturity stage, oil-gas coexistence at the mature stage, and gas generation at the high maturity stage. While the C(2)k(4) source rocks were marginally mature and mature at 273 Ma and 248 Ma, respectively. Bitumen geochemical analysis and balanced cross-sections indicate that a subsequent paleo-reservoir adjustment and structural alteration resulted from multiple uplifts and faulting activities after early hydrocarbon generation in the Ounan Sag. The adjustment could be confirmed using hydrocarbon inclusions with an abnormally low salinity and homogenization temperature and fluorescence. Secondary oil and gas reservoirs in the Ounan Sag and the deep-buried oil and gas reservoir in the structurally stable Delingha Sag are considered to be potential exploration targets.
查看更多>>摘要：The combination of horizontal well drilling and staged fracturing technology is one of the most effective ways to increase unconventional oil and gas production. The orientation of the horizontal wellbore is mostly considered to be consistent with the minimum horizontal principal stress in the numerical simulation. However, horizontal wellbores are usually at an angle to the minimum principal stress in field operations. In this case, the dynamic growth of the fractures will be limited during subsequent perforation and fracturing operations. Therefore, the objective of this paper is to build an effective numerical model to investigate the propagation of hydraulic fractures in the case of a horizontal wellbore that is not co-linear with the minimum horizontal principal stress. The extended finite element method is applied for multi-stage fracturing flow-solid coupling in horizontal wells. In addition, the effect of sequential and alternate fracturing on the propagation of fractures in horizontal wells was compared. The simulation results show that fracture growth on one side of the horizontal wellbore is severely restricted as the pinch angle increases. It is most evident in sequential fracturing mode while increasing the fracture spacing is one of the effective ways to reduce inter-fracture interference. Meanwhile, compared to sequential fracturing, alternate fracturing is more effective in reducing inter-fracture interference, allowing for effective fracturing at closer fracture spacing. Finally, the density of the fracturing section has been increased, and a higher reservoir reconstruction area can be obtained. This work, for the first time, evaluates the dynamic propagation of fractures when the horizontal wellbore is not collinear with the minimum horizontal principal stress orientation and helps operators to optimize the fracturing process.
查看更多>>摘要：Aiming at the problem of the unclear microscopic interaction mechanisms between carbon dioxide (CO2) and reservoir rocks and fluids during CO2 huff-n-puff in ultra-heavy oil reservoirs. In this study, scanning electron microscope (SEM) and visual displacement experiment were used to understand the mechanisms among them. Then, the interaction experiment and numerical simulation between CO2 and heavy oil were carried out to verify the feasibility of CO2 huff-n-puff to improve the recovery of ultra-heavy oil reservoirs. The results showed that: a) Within a certain range of pressure, the corrosion ability of carbonated water formed by CO2 and formation water gradually increased as pressure increased. When the pressure increased from 6 MPa to 12 MPa, the average corrosion rate increased from 0.36% to 0.49%. b) CO2 can effectively improve the pore structure and permeability of rocks. The injection of CO2 caused hydration expansion, surface particle peeled off, deepening of depression, the appearance of corrosion pits and cracks, and cracks extending as pressure increased. Under the pressure of 6 MPa and 12 MPa, the permeability increased by 14.67% and 41.86%, respectively. c) CO2 injection made the heavy oil form amount of dispersed secondary foam oil, which greatly reduced the viscosity of crude oil and increased the expansion ability of heavy oil. When the pressure increased to 12 MPa, the volume coefficient and viscosity reduction rate reached 1.18 and 99.62%. d) The displacement experiment demonstrated that the displacement efficiency of CO2 was 23.09% higher than water flooding. e) According to the results of numerical simulation, during five cycles of CO2 huff-n-puff, the reservoir pressure rose from 3481 KPa at the end of depletion production to 6187 KPa, which showed that pressure was effectively maintained; The viscosity of the heavy oil decreased from 12825cp to 3280.49cp, and the 10-year recovery factor was 13.08%. The understanding of the micro-action mechanism could have certain guiding significance for improving the development effect of CO2 huff-n-puff in the ultra-heavy oil reservoirs.
查看更多>>摘要：The physics-informed neural network (PINN) is a general deep learning framework for simulating physical processes and surrogate modeling without labeled data. The basic idea is to formulate the loss function according to the governing PDEs such that the neural network (NN) can be trained to minimize the PDE residual along with other misfits such as initial and boundary conditions. Following PINN, various networks have been developed for simulating steady and transient flows with or without labeled data. However, according to literature review, it is still not clear how to use NNs to simulate transient Darcy flows in highly heterogeneous reservoir models with source/sink terms in the absence of labeled data. In the current study, a physics informed deep convolutional neural network (PIDCNN) architecture for simulating and predicting such flows is presented. Convolutional neural network is found to be more efficient than fully-connected neural network since 2D variables can be regarded as images. The finite volume discretization scheme is adopted to build the loss function to approximate the PDE residual such that flux continuity between neighboring cells of different properties can be implemented conveniently using the two-point flux approximation. Test cases are used to show that PIDCNN can accurately simulate transient Darcy flows in homogeneous and heterogeneous reservoirs. Further, it is demonstrated that PIDCNN can be trained as a surrogate to predict the transient flow fields of reservoir models not included in training. In addition, the CNN structure in the current study can be trained as a surrogate with labels for a particular output for better accuracy. A workflow is presented to demonstrate that CNN can be trained as an accurate surrogate for production rates using labels generated by the PIDCNN-based solver such that the entire workflow is external-label-free.
查看更多>>摘要：We study the applicability of Generative Adversarial Networks (GANs) for generating the synthetic data related to well construction and geological characterisation of the near-wellbore area. We focus on 1D mud logs (time series) and 2D core images. GANs are known to have difficulties with their quality assessment in general. Moreover, generic GAN's performance assessment methods cannot be suitable for the petroleum domain. A petroleum engineer expects the GANs to generate data with specific physical and geological properties, not just a colourful picture. We have trained over 40 GAN models and generated synthetic data with them. Then, we have involved several experts to analyse the generated data in order to address the question of whether it is possible to substitute human analysis with a mathematical metric. We found that some quantitative mathematical metrics can represent our experts' perceptions. In particular, we show that for 2D core images, Mode Score metric with standard inception v3 model is the best proxy for all considered qualitative metrics of expert's perception according to the Kendall correlation (for two qualitative metrics the correlation is strong, the absolute value is above 0.7, and for other two it is moderate, the absolute value is between 0.5 and 0.7); for mud logs time-series, Mode Score and Frechet Inception Distance with the InceptionTime model provide the strong (above 0.7) correlation with objects reconstruction quality, whereas Inception Score has almost strong correlation (with Kendalls'-tau coefficient 0.69) with experts' perception of objects generation quality. With these results, experts manual annotation of generated objects during GAN model selection process can be reduced to calculating the corresponding quantitative metrics.
NSTL
Elsevier