Single-well productivity is a crucial metric for assessing the effectiveness of petroleum reservoir development. The accurate prediction of productivity is essential for achieving the efficient and economical development of oil–gas reservoirs. Traditional productivity prediction methods (empirical formulae and numerical simulation) are limited to specific reservoir types. There are few influencing factors, and a large number of ideal assumptions are made for the assumed conditions when predicting productivity. The application scenario is ideal. However, in tight oil reservoirs, numerous factors affect productivity, and their interactions exhibit significant complexity. Continuing to use traditional reservoir productivity prediction methods may result in significant calculation errors and lead to economic losses in oilfield development. To enhance the accuracy of tight reservoir productivity predictions and achieve economical and efficient development, this paper investigates the tight reservoir in the WZ block of the Beibuwan area, considering the impact of geological and engineering factors on productivity; the random forest tree and Spearman correlation coefficient are used to analyze the main influencing factors of productivity. The back propagation neural network optimized by particle swarm optimization was employed to develop a productivity prediction model (PSO-BP model) for offshore deep and ultra-deep tight reservoirs. The actual test well data of the oilfield are substituted into this model. The analysis results of the example application yielded an RMSE of 0.032, an MAE of 1.209, and an R² value of 0.919. Compared with traditional productivity prediction methods, this study concludes that the model is both reasonable and practical. The calculation speed is faster and the calculation result is more accurate, which can greatly reduce productivity errors. The model constructed in this paper is well suited for predicting the productivity of tight reservoirs within the WZ block. It offers substantial guidance for predicting the productivity of similar reservoirs and supports the economical and efficient development of petroleum reservoirs.

Since the “13th Five-Year Plan”, the exploration of large-scale structural oil and gas reservoirs in the Bohai oilfield has become more complex, and the exploration of igneous oil and gas reservoirs has become the focus of current attention. At present, igneous rock reservoir fluid identification methods are mainly based on the evaluation method of logging single parameter construction, which is primarily a qualitative identification due to lithology, physical property, and engineering factors. Accurate acquisition of interference logging data, and multi-parameter coupling and recording coupling methods are few, lacking systematic and comprehensive evaluation and analysis of logging data. Since conventional logging data in the study area have difficulty accurately and quickly identifying reservoir fluid properties, a systematic analysis was conducted of three factors: lithology, physical properties, and engineering, as well as a variety of logging parameters (gas measurement, three-dimensional quantitative fluorescence, geochemical, FLAIR, etc.) that can reflect fluid properties were integrated. Based on parameter sensitivity analysis, the quantitative characterization index FI of multi-parameter coupling fluid identification was established using the data from testing, sampling, and laboratory testing to develop the identification standard. The sensitivity analysis and optimization of characteristic parameters were carried out by integrating the data reflecting fluid properties such as gas surveys, geochemical data, and related logging data. Combined with gas logging-derived parameters and improved engineering parameters (the value of alkanes released by rock cracking per unit volume ${\mathrm{C}}_{\mathrm{a}\mathrm{d}\mathrm{j}\mathrm{u}\mathrm{s}\mathrm{t}}$, ${\mathrm{C}}_1$ abnormal multiple values, three-dimensional quantitative fluorescence correlation factor N), the fluid properties were identified, evaluation factors were constructed based on factor analysis, and fluid identification interactive charts were established. By analyzing test wells in the PL9-1 well area, the results of comparison test data are more reliable. Compared with conventional methods, this method reduces the dependence of a single parameter by synthesizing multiple parameters and reduces the influence of lithology, physical properties, and engineering parameters on fluid identification. It is more reasonable and practical. It can accurately and quickly identify the fluid properties of igneous rock reservoirs in the study area. It has a guiding significance for improving the accurate evaluation of logging data and increasing exploration benefits.

This study addresses the challenge of rapidly and accurately predicting the production of test wells in offshore tight oil reservoirs, specifically within the L Formation of the Beibu Basin. This challenge is particularly pronounced in situations where drill stem tests are limited and evaluating each untested well layer is difficult. To achieve this objective, we analyzed fifteen typical test wells in the L Formation, taking into account both geological and engineering factors. Initially, Pearson correlation analysis, partial correlation analysis, and grey relational analysis were used to identify the main production factors. Based on these analyses, two types of production prediction models were developed: one employing the comprehensive production index method and the other utilizing the production coefficient method. The research identified effective permeability, porosity, oil saturation, and shale content as the main production factors for the test wells in the study area. The model verification results showed that the comprehensive production index model performs effectively for the L Formation, with an average prediction error of 20.40% compared to the actual production values. This research is significant for optimizing and stabilizing production in tight oil reservoirs.