Search

This paper presents an optimal design of alkaline–surfactant–polymer (ASP) flooding and an experimental analysis on the effects of ASP components under low formation salinity, where the assignment of salinity gradients and various phase types are limited. The phase behavior and coreflooding tests confirmed the ASP formula is optimal, i.e., 1 wt % sodium carbonate (Na2CO3) as the alkaline, 1:4 weight ratio for linear alkylbenzene sulfonate (LAS) and dioctyl sulfosuccinate (DOSS) as a surfactant, 5 wt % diethylene glycol monobutyl ether (DGBE) as a co-solvent, and hydrolyzed polyacrylamide (HPAM) as a polymer. The salinity scan was used to determine that the optimum salinity was around 1.25 wt % NaCl and its solubilization ratio was favorable, i.e., approximately 21 mL/mL. The filtration ratio determines the polymer concentrations, i.e., 3000 or 3300 mg/L, with a reduced risk of plugging through pore throats. The coreflooding test confirmed the field applicability of the proposed ASP formula with an 86.2% recovery rate of residual oil after extensive waterflooding. The optimal design for ASP flooding successfully generated phase types through the modification of salinity and can be applicable to the low-salinity environment. [ABSTRACT FROM AUTHOR]

This paper experimentally analyzes the chemical additives, i.e., methanol and ethanol, as alcohol solvents, and acetone as a ketone solvent, and the temperature influencing the minimum miscibility pressure (MMP) that is essential to design miscible CO2 flooding at an oil field, the South Sumatra basin, Indonesia. The experiments were designed to measure CO2-oil interfacial tension with the vanishing interfacial tension (VIT) method in the ranges up to 3000 psi (208.6 bar) and 300 degrees Celsius. The experiment results show that lower temperatures, larger solvent volumes, and the acetone were effective in reducing MMP. The acetone, an aprotic ketone solvent, reduced MMP more than the methanol and the ethanol in the CO2-oil system. The high temperature was negative to obtain the high CO2 solubility into the oil as well as the lower MMP. The experimental results confirm that the aprotic ketone solvent could be effective in decreasing the MMP for the design of miscible CO2 flooding at the shallow mature oilfields with a low reservoir temperature. [ABSTRACT FROM AUTHOR]

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 k-means 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. • A generative geomodeling framework classified by flow responses was developed. • A convolutional variational autoencoder (VAE) generates plausible geomodels. • The VAE contributes to preserving geological realism. • The workflow is applicable to history matching problems without inverse schemes. [ABSTRACT FROM AUTHOR]