The Fragmentation in a Protoplanetary Disk and the Properties of the Resultant Self-Gravitating Clumps

We study the fragmentation properties in the protoplanetary disk and properties of the resultant self-gravitating clumps using our newly constructed disk model. Our disk model includes the mass inflall term from a molecular cloud core and the photoevaporation winds effect. We adopt the conventional fragmentation criterion to judge whether a protoplanetary disk can fragment. In this work, we follow our previous work to investigate the properties of the resultant self-gravitating clumps. In our calculation, the initial masses of the resultant self-gravitating clumps lie in the range of tens of M_J to more than one hundred of M_J, where M_J is the Jupiter mass. These initial masses can seemingly account for the masses of extrasolar planets in magnitude. We also calculate the subsequent gas accretion of clumps in 1.27 × 10⁴ yr after the formation of self-gravitating clumps. We find that the subsequent gas accretion of self-gravitating clumps is very efficient, and the clump masses grow to hundreds of M_J and the physical radii R_c of clumps increase to about 10 AU. Additionally, we also calculate the orbital migration of clumps. We find that most clumps have short migration timescale to be accreted onto the protostar, and only a small fraction of clumps have long migration timescale (>10⁶ yr) to successfully become gas giant planets. These results are consistent with previous studies.     

Ensemble clustering for efficient robust optimization of naturally fractured reservoirs

In the development of naturally fractured reservoirs (NFRs), the existence of natural fractures induces severe fingering and breakthrough. To manage the flooding process and improve the ultimate recovery, we propose a numerical workflow to generate optimal production schedules for smart wells, in which the inflow control valve (ICV) settings can be controlled individually. To properly consider the uncertainty introduced by randomly distributed natural fractures, the robust optimization would require a large ensemble size and it would be computationally demanding. In this work, a hierarchical clustering method is proposed to select representative models for the robust optimization in order to avoid redundant simulation runs and improve the efficiency of the robust optimization. By reducing the full ensemble of models into a small subset ensemble, the efficiency of the robust optimization algorithm is significantly improved. The robust optimization is performed using the StoSAG scheme to find the optimal well controls that maximize the net-present-value (NPV) of the NFR’s development. Due to the discrete property of a natural fracture field, traditional feature extraction methods such as model-parameter-based clustering may not be directly applicable. Therefore, two different kinds of clustering-based optimization methods, a state-based (e.g., s _w profiles) clustering and a response-based (e.g., production rates) clustering, are proposed and compared. The computational results show that the robust clustering optimization could increase the computational efficiency significantly without sacrificing much expected NPV of the robust optimization. Moreover, the performance of different clustering algorithms varies widely in correspondence to different selections of clustering features. By properly extracting model features, the clustered subset could adequately represent the uncertainty of the full ensemble.