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.

Perforation technology is often used to improve the productivity of horizontal wells in oilfield exploitation. During the perforation process, the formation seepage mode and productivity will change accordingly whether the contaminated zone is shot through. If we continue to use the previous productivity formula, it will cause a large calculation error and bring economic loss to the development of oil and gas fields. Firstly, based on the principle of equivalent seepage resistance, the reservoir-hole inflow profile in these two cases is analyzed in detail, and the reservoir-hole seepage model is constructed in different regions. The perforated horizontal well section is divided into N micro-unit sections, and the pressure drop model in the wellbore is constructed using the fluid mechanics theory. A new perforated horizontal well productivity prediction model is then created by coupling the reservoir-hole seepage model with the pressure drop model in the wellbore as a whole to accurately reflect the production performance of the perforated horizontal well. Through comparison and verification, it is concluded that the calculation results of the model are more precise, which can greatly reduce the productivity error. This method is reasonable and practical. When the oilfield’s actual well data is substituted into this model, it is discussed and analyzed that the reservoir’s physical characteristics, contamination level, and perforation completion parameters all have an impact on the productivity of horizontal wells, with the original formation’s permeability and reservoir contamination thickness having the most pronounced effects. These findings may effectively direct the design of technological processes and performance impact prediction.

Classification of gas wells is an important part of optimizing development strategies and increasing the recovery. The original classification standard of gas wells in the Sulige gas field has weak regularity of each parameter, large overlapping range of classification results, serious discrepancy between the dynamic and static, and low efficiency of manual classification. Aiming at this problem, this paper establishes a set of dynamic and static integrated classification model of tight sandstone gas wells in Sulige based on XGBoost algorithm. After comparison and verification, it is proved to be accurate and reliable. The model can be substituted into the static and dynamic characteristic parameters at the same time to complete the importance ranking of classification features and model training, and realize the dynamic and static integration classification of Sulige gas well. The model is applied to 553 gas wells in S block, and it is concluded that the main factors affecting the classification of gas wells are initial daily production, effective thickness of a gas layer, formation permeability, original formation pressure, and porosity. The main factors affecting the classification of class I and class II wells are initial daily production and permeability, and the main factors affecting the classification of class III wells are initial daily production and the effective thickness of the gas layer. This method improves the effectiveness of gas well classification, reduces subjectivity, and the classification results are in line with the actual situation of the field, which has guiding significance for the classification management of gas wells and the formulation of development countermeasures.

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.

Drilling and completion processes can often result in reservoir contamination around the wellbore, leading to decreased oil and gas productivity and significant economic losses for the oil field. This issue is particularly complex in sandstone reservoirs with low porosity and permeability horizontal wells, wherein traditional models have limited accuracy and applicability due to numerous unknown parameters. To address this challenge, this study focuses on non-uniform pollution around horizontal wells and proposes a new approach to divide the horizontal well pollution area into N micro-element sections. By establishing a seepage differential equation and employing the similar flow substitution method, we construct models for the pollution skin coefficient of each micro-element section as well as the total pollution skin coefficient. Furthermore, we combine empirical equation models and an oscillation-decreasing function model to develop a pollution radius distribution model that encompasses linear, parabolic, exponential, and logarithmic patterns. Through these advancements, we can realize a comprehensive reservoir damage assessment method. It is verified that the calculation error of this model is very small, and the influence of skin effect and reservoir anisotropy and the radius distribution of various heterogeneous pollution zones are fully considered. These findings indirectly suggest the rationality and practicality of the model presented in this paper. By incorporating actual gas well data into this model, it has been determined through discussion and analysis that the exponential distribution of the pollution radius has the greatest impact on the pollution skin factor along the horizontal well, from the heel to the toe. Increasing the pollution radius and decreasing the pollution permeability both result in an increase in the skin factor of the micro-segment and the total pollution skin factor of the horizontal well. However, compared to the pollution permeability, the radius of the pollution zone has a relatively minor effect on the total pollution skin factor. The proposed technique aims to serve as a valuable tool in optimizing and designing stimulation measures aimed at boosting production and minimizing formation damage. Through evaluation and analysis to reduce risks, protect reservoirs and extend well life, reduce costs, and enhance technical capabilities and economic benefits.