ABSTRACT
Carbonates have great potential for development, but the use of propped hydraulic fracturing or acid fracturing stimulation types alone has limited effectiveness. This study collected four sets of rock samples from three carbonate reservoirs: dolomite, limy dolomite, and limestone. The laboratory analysis focuses on propped hydraulic fracturing, acid fracturing, and acid fracturing plus proppants of these rock types. The aim is to assess acid erosion, proppant embedment depth, and the impact of varying proppant sizes and acid injection on fracture conductivity. The results showed that acid fracturing substantially enhanced the hydraulic conductivity of the three rocks. The embedded depth of the small-sized proppant was greater, but the fracture conductivity of the large-sized proppant was greater. Under the conditions of the actual pressure of formation of 55.2 MPa in the target reservoir, using 70/140 mesh proppant, 20% thickened acid, and an injection rate of 20 mL/min: for the dolomite-type rock, the propped hydraulic fracturing method had the highest fracture conductivity, which reached 151.17 D·cm. For the limy dolomite-type rock, the acid fracturing plus proppant experiment had the highest fracture conductivity, which was 157.26 D·cm. For limestone-type rocks, acid fracturing had the highest fracture conductivity of 210.39 D·cm. This study helps to elucidate the mechanism of different stimulation types and provides useful guidance for the more effective development of carbonate oil and gas reservoirs.
ABSTRACT
Nowadays, the impact of engineered nanoparticles (NPs) on human health and environment has aroused widespread attention. It is essential to assess and predict the biological activity, toxicity, and physicochemical properties of NPs. Computation-based methods have been developed to be efficient alternatives for understanding the negative effects of nanoparticles on the environment and human health. Here, a classification-based structure-activity relationship model for nanoparticles (nano-SAR) was developed to predict the cellular uptake of 109 functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2). The norm index descriptors were employed for describing the structure characteristics of the involved nanoparticles. The Random forest algorithm (RF), combining with the Recursive Feature Elimination (RFE) was employed to develop the nano-SAR model. The resulted model showed satisfactory statistical performance, with the accuracy (ACC) of the test set and the training set of 0.950 and 0.966, respectively, demonstrating that the model had satisfactory classification effect. The model was rigorously verified and further extensively compared with models in the literature. The proposed model could be reasonably expected to predict the cellular uptakes of nanoparticles and provide some guidance for the design and manufacture of safer nanomaterials.
