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Summary Slickwater fracturing has become one of the most leveraging completion technologies in unlocking hydrocarbon in unconventional reservoirs. In slickwater treatments, proppant transport becomes a big concern because of the inefficiency of low-viscosity fluids to suspend the particles. Many studies have been devoted to proppant transport experimentally and numerically. However, only a few focused on the proppant pumping schedules in slickwater fracturing. The impact of proppant schedules on well production remains unclear. The goal of our work is to simulate the proppant transport under real pumping schedules (multisize proppants and varying concentration) at the field scale and quantitatively evaluate the effects of proppant schedules on well production for slickwater fracturing. The workflow consists of three steps. First, a validated 3D multiphase particle-in-cell (MP-PIC) model has been used to simulate the proppant transport at real pumping schedules in a field-scale fracture (180-m length, 30-m height). Second, we applied a propped fracture conductivity model to calculate the distribution of propped fracture width, permeability, and fracture conductivity. In the last step, we incorporated the fracture geometry, propped fracture conductivity, and the estimated unpropped fracture conductivity into a reservoir simulation model to predict gas production. Based on the field designs of pumping schedules in slickwater treatments, we have generated four proppant schedules, in which 100-mesh and 40/70-mesh proppants were loaded successively with stair-stepped and incremental stages. The first three were used to study the effects of the mass percentages of the multisize proppants. From Schedules 1 through 3, the mass percentage of 100-mesh proppants is 30, 50, and 70%, respectively. Schedule 4 has the same proppant percentage as Schedule 2 but has a flush stage after slurry injection. The comparison between Schedules 2 and 4 enables us to evaluate the effect of the flush stage on well production. The results indicate that the proppant schedule has a significant influence on treatment performance. The schedule with a higher percentage of 100-mesh proppants has a longer proppant transport distance, a larger propped fracture area, but a lower propped fracture conductivity. Then, the reservoir simulation results show that both the small and large percentages of 100-mesh proppants cannot maximize well production because of the corresponding small propped area and low propped fracture conductivity. Schedule 2, with a median percentage (50%) of 100-mesh proppants, has the highest 1,000-day cumulative gas production. For Schedule 4, the flush stage significantly benefits the gas production by 8.2% because of a longer and more uniform proppant bed along the fracture. In this paper, for the first time, we provide both the qualitative explanation and quantitative evaluation for the impact of proppant pumping schedules on the performance of slickwater treatments at the field scale by using an integrated numerical simulation workflow, providing crucial insights for the design of proppant schedules in the field slickwater treatments. 

Disassembling stripped binaries is a prominent challenge for binary analysis, due to the interleaving of code segments and data, and the difficulties of resolving control transfer targets of indirect calls and jumps. As a result, most existing disassemblers have both false positives (FP) and false negatives (FN). We observe that uncertainty is inevitable in disassembly due to the information loss during compilation and code generation. Therefore, we propose to model such uncertainty using probabilities and propose a novel disassembly technique, which computes a probability for each address in the code space, indicating its likelihood of being a true positive instruction. The probability is computed from a set of features that are reachable to an address, including control flow and data flow features. Our experiments with more than two thousands binaries show that our technique does not have any FN and has only 3.7% FP. In comparison, a state-of-the-art superset disassembly technique has 85% FP. A rewriter built on our disassembly can generate binaries that are only half of the size of those by superset disassembly and run 3% faster. While many widely-used disassemblers such as IDA and BAP suffer from missing function entries, our experiment also shows that even without any function entry information, our disassembler can still achieve 0 FN and 6.8% FP.