## 1 Introduction  
Delta depositional systems are widely developed in fault basins, providing favorable conditions for hydrocarbon generation, accumulation, and traps, and are natural places of oil and gas enrichment [1]. The delta front reservoir is a major reservoir type in China. The frequent swing of underwater distributary channels gradually increases the width of sand bodies, forming complex channel sand bodies, which is the best reservoir group of delta front [2]. Although the features of the distribution of the composite channels reflect the macro-heterogeneity of the reservoir to some extent, studies on the fine-scale heterogeneity of single-channel sand body reservoirs are insufficient. Due to the heterogeneity of single-channel sand reservoirs and the existence of interlayers, underground fluid flow is blocked by vertical and lateral barriers, resulting in the enrichment of the remaining oil in composite sand [3, 4, 5]. Nowadays, most oilfields have entered the advanced stage of exploitation, with a low rate of recovery and a strong non-homogeneity of reservoirs, so the study of residual oil distribution and the enhancement of recovery has become a common concern for all oilfields. Understanding how the residual oil is distributed is key to improving recovery. Its controlling factors include two main aspects, development factors and geological factors. The geological factors are manifested in reservoir heterogeneity. In braided river delta sedimentary systems, sand deposits in underwater distributary channels are the main type, and are characterized by channel curvature, a continuous swing, and rapid lateral change [6]. The description of channel width constitutes a difficulty in the study of this type of reservoir configuration [7]. Therefore, it is necessary to start from the identification of a single channel, and, step by step, to dissect and analyze the composite channel sand body heterogeneity, so as to have a higher prediction of the residual oil distribution and improve the extraction efficiency [8; 9; 10]. The width of the channel sand body is one of the key factors to design the well location scheme, and the key to identify a single channel is to accurately identify the channel boundary. Therefore, in order to identify a single channel, the width of the channel must be determined. At present, various methods have been used to interpret the channel scale. [PERSON] used sedimentological theory, combined with the core information and well-logging results, and quantitatively identified a single channel sand body in a submerged catchment channel along the fan delta front [11]. [PERSON] used a multi-parameter analysis method to conduct anatomical analysis on the single sand body configuration of the underwater distributary channel belt of the delta front [12]. By using physical simulation and changing control factors, [PERSON] studied the development process of underwater distributary channels in lake deltas and systematically analyzed the scale characteristics of underwater distributary channels [13]. [PERSON] conducted an in-depth analysis of modern underwater distributary channels by using a remote sensing information image method, and discussed their formation conditions and channel characteristics [14]. [PERSON] used multi-point geostatistics to simulate the underwater diversion channels along the delta force, and calculated the channel drilling rate to determine the channel width [15]. Although these methods have made a certain contribution to the identification of the river size, this kind of river width based on manual de-identification is mostly subjective, and therefore these methods can only determine a general range, which is more dependent on the researcher's empirical knowledge, and cannot accurately and quickly identify the river size information. With the rapid development of deep learning, it has become a hot research topic in recent years to apply deep learning to the reservoir modelling field.