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The accurate prediction of gas well production is one of the key factors affecting the economical and efficient development of tight gas wells. The traditional oil and gas well production prediction method assumes strict conditions and has a low prediction accuracy in actual field applications. At present, intelligent algorithms based on big data have been applied in oil and gas well production prediction, but there are still some limitations. Only learning from data leads to the poor generalization ability and anti-interference ability of prediction models. To solve this problem, a production prediction method of tight gas wells based on the decline curve and data-driven neural network is established in this paper. Based on the actual production data of fractured horizontal wells in three tight gas reservoirs in the Ordos Basin, the prediction effect of the Arps decline curve model, the SPED decline curve model, the MFF decline curve model, and the combination of the decline curve and data-driven neural network model is compared and analyzed. The results of the case analysis show that the MFF model and the combined data-driven model have the highest accuracy, the average absolute percentage error is 14.11%, and the root-mean-square error is 1.491, which provides a new method for the production prediction of tight gas wells in the Ordos Basin. [ABSTRACT FROM AUTHOR]

The production of tight gas wells decreases rapidly, and the traditional method is difficult to accurately predict the production of tight gas wells. At present, intelligent algorithms based on big data have been applied in oil and gas well production prediction, but there are still some technical problems. For example, the traditional error back propagation neural network (BP) still has the problem of finding the local optimal value, resulting in low prediction accuracy. In order to solve this problem, this paper establishes the output prediction method of BP neural network optimized with the sparrow search algorithm (SSA), and optimizes the hyperparameters of BP network such as activation function, training function, hidden layer, and node number based on examples, and constructs a high-precision SSA-BP neural network model. Data from 20 tight gas wells, the SSA-BP neural network model, Hongyuan model, and Arps model are predicted and compared. The results indicate that when the proportion of the predicted data is 20%, the SSA-BP model predicts an average absolute mean percentage error of 20.16%. When the proportion of forecast data is 10% of the total data, the SSA-BP algorithm has high accuracy and high stability. When the proportion of predicted data is 10%, the mean absolute average percentage error is 3.97%, which provides a new method for tight gas well productivity prediction. [ABSTRACT FROM AUTHOR]

Summary: MiRNAs have been reported to be the key regulators involving a wide range of biological processes in diverse plant species, but their functions in switchgrass, an important biofuel and forage crop, are largely unknown. Here, we reported the novel function of miR528, which has expanded to four copies in switchgrass, in controlling biomass trait of tillering number and regrowth rate after mowing. Blocking miR528 activity by expressing short tandem target mimic (STTM) increased tiller number and regrowth rate after mowing. The quadruple pvmir528 mutant lines derived from genome editing also showed such improved traits. Degradome and RNA‐seq analysis, combined with in situ hybridization assay revealed that up‐regulation of two miR528 targets coding for Cu/Zn‐SOD enzymes, might be responsible for the improved traits of tillering and regrowth in pvmir528 mutant. Additionally, natural variations in the miR528‐SOD interaction exist in C3 and C4 monocot species, implying the distinct regulatory strength of the miR528‐SOD module during monocot evolution. Overall, our data illuminated a novel role of miR528 in controlling biomass traits and provided a new target for genetic manipulation‐mediated crop improvement. [ABSTRACT FROM AUTHOR]