In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer. During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion (the method proposed in this paper) performs best with the lowest mean absolute error (MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.

To assess whether a development strategy will be profitable enough, production forecasting is a crucial and difficult step in the process. The development history of other reservoirs in the same class tends to be studied to make predictions accurate. However, the permeability field, well patterns, and development regime must all be similar for two reservoirs to be considered in the same class. This results in very few available experiences from other reservoirs even though there is a lot of historical information on numerous reservoirs because it is difficult to find such similar reservoirs. This paper proposes a learn-to-learn method, which can better utilize a vast amount of historical data from various reservoirs. Intuitively, the proposed method first learns how to learn samples before directly learning rules in samples. Technically, by utilizing gradients from networks with independent parameters and copied structure in each class of reservoirs, the proposed network obtains the optimal shared initial parameters which are regarded as transferable information across different classes. Based on that, the network is able to predict future production indices for the target reservoir by only training with very limited samples collected from reservoirs in the same class. Two cases further demonstrate its superiority in accuracy to other widely-used network methods.

This report summarizes the recent findings on gas transport mechanisms in shale gas reservoir by pore network modelling. Multi-scale pore network model was developed to accurately characterize the shale pore structure. The pore network single component gas transport model was established considering the gas slippage and real gas property. The gas transport mechanisms in shale pore systems were elaborated on this basis. A multicomponent hydrocarbon pore network transport model was further proposed considering the influences of capillary pressure and fluid occurrence on fugacity balance. The hydrocarbon composition and pore structure influences on condensate gas transport were analyzed. These results provide valuable insights on gas transport mechanisms in shale gas reservoir.

Hydraulic fracturing is a pivotal technology in the development of unconventional tight reservoirs, in which accurate monitoring of fracture parameters is significant. This paper proposes an improved differential evolution algorithm (EMDE) to calculate the Effective Propped Volume (EPV) accurately. The forward simulation results demonstrate that when the transmitting source plane is in a particular position, the relationship between signals and a specific parameter is the most obvious, providing a basis for the application of inversion algorithms. Furthermore, the difference between the population center and the individual is added to accelerate the convergence of the EMDE algorithm. A simplified selection strategy of the simulated annealing algorithm is used to enhance the convergence speed and the ability to find the global optimal value of the objective function simultaneously. The one-stage and two-stages inversion strategies are designed to calculate the parameters. In the two-stage inversion, the second-stage is constrained by the forward simulation and the first-stage results. It indicates that the errors of the two-stages inversion can be controlled within 5%. Through the inversion simulation proposed in this paper, the feasibility of the electromagnetic method to monitor the EPV is verified, and it provides a theoretical guidance for subsequent fracturing construction adjustments.