In most cases, reservoir properties at one certain depth in the layer can be explicated by logging signals at just this depth point. In fact, the properties of complex reservoirs are often implicated in logging signals from the whole adjacent region of this certain depth point. So far, there is no effective way to solve this problem completely. For the first time, this letter tried to build a fully convolutional neural network (FCNN) to detect hydrocarbon from logging signals for the tight gas reservoir of Ordos Basin. The FCNN was based on a well-designed VGG-net. The prediction comparison between the empirical approach (EMA) and FCNN was implemented on 48 layers. The accuracy of FCNN was about 87.5%, which was higher than that of the EMA (75.0%). FCNN provided more reliable gas testing recommendations, especially when thin layers led to complex reservoir conditions. Deep learning (DL) has been proven to be an automatic feature extraction and direct hydrocarbon detection approach from logging signals. We are looking forward to its improvement and development in geophysics.

The planning of optimal grasps is an important problem in robotics which has been investigated by many researchers. The large number of available methods has made it difficult to discern those which plan a grasp with good overall performance, i.e., one with high strength, insensitivity to positioning errors, and ease of computation. In this paper, a new grasp planning method is introduced and compared to three existing planning methods using three such metrics. A new metric for measuring the sensitivity of a grasp to positioning errors is also introduced. Since grasp planning is much simpler in 2D, and 2D grasps are applicable to many 3D objects, the four methods involve only a 2D analysis. The methods are applied to a set of six polygonal objects, ranging from 3 sided to 74 sided, and their overall performance is compared. The benchmarking procedure is readily applicable to other grasp planning methods.