The prediction and distribution of reservoir porosity and permeability are of paramount importance for the exploration and development of regional oil and gas resources. In order to optimize the prediction methods of porosity and permeability and better guide gas field development, it is necessary to identify the most effective approaches. Therefore, based on the extreme gradient boosting (XGBoost) algorithm, laboratory test data of the porosity and permeability of cores from the southern margin of the Ordos Basin were selected as the target labels, conventional logging curves were used as the input feature variables, and the mean absolute error (MAE) and the coefficient of determination (R²) were used as the evaluation indicators. Following the selection of the optimal feature variables and optimization of the hyper-parameters, an XGBoost porosity and permeability prediction model was established. Subsequently, the innovative application of homogeneous clustering (K-means) data preprocessing was applied to enhance the XGBoost model’s performance. The results show that logarithmically preprocessed (LOG(PERM)) target labels enhanced the performance of the XGBoost permeability prediction model, with an increase of 0.26 in its test set R². Furthermore, the application of K-means improved the performance of the XGBoost prediction model, with an increase of 0.15 in the R² of the model and a decrease of 0.017 in the MAE. Finally, the POR_0/POR_1 grouped porosity model was selected as the final predictive model for porosity in the study area, and the Arctan(PERM)_0/Arctan(PER0M)_1 grouped model was selected as the final predictive model for permeability, which has better prediction accuracy than logging curves. The combination of K-means and the XGBoost modeling method provides a new approach and reference for the efficient and relatively accurate evaluation of porosity and permeability in the study area.

Key issues for smart fields have been discussed in this paper, including the main features, trends, solutions, construction elements and technical differences in China and abroad. Smart fields include three elements: real-time monitoring, modeling, decision-making and implementation. Their main features are real time, integration and continuity. The essence of smart fields is the optimization based on the model system, including 4 levels: single-well optimization, production optimization, reservoir optimization and optimization of the overall assets. And the overall system optimization is the ultimate objective. For smart fields, the smart is a method of constructing smart fields and efficient development is its purpose. Data is the basis and key of smart fields. The essence of smart field decision-making is based on the model. Smart fields can not go without the support of high-tech industry. The difference of smart fields technologies between home and abroad mainly lies in the oil and gas exploration and development mode and management concepts, technical means and research team, the timeliness of decision making, the responsibilities of different professionals and development objects.

Studying the gas–water distribution characteristics is essential in guiding the efficient development of gas fields. The relationship between gas and water in the Sudong 41-33 Block is complicated and has not been adequately researched. In recent years, gas wells have suffered from increased water/gas ratios and significant liquid loadings, which greatly affect the development of the block. A comprehensive analysis of formation water, log interpretation, and production data was conducted to determine the gas–water distribution characteristics and main controlling factors in the Sudong 41-33 Block. The findings indicate the following. (1) The formation water in the study area consists mainly of CaCl₂ brine with high total dissolved solids (TDS) (with an average value of 36.06 g/L). The hydrochemical characteristics indicate that the formation water is typical sedimentary buried water under well-sealing conditions, which is markedly different from shallow river water and seawater. (2) The formation water can be categorized into three types: edge–bottom water under the gas layer (Type I), stagnant water in tight sandstone (Type II), and isolated lenticular water (Type III). The water layer distribution in the plane is mainly concentrated in the northwest region, whereas it is dispersed in other regions. On the vertical, the water layer mainly appears in P₂x_8–1, P₂x_8–2, and P₁s₂ Members. (3) The physical properties of the reservoir, hydrocarbon generation intensity (HGI), source rock–reservoir relationship, and mini-structure are the main factors affecting the gas–water distribution in the study area. Based on the clarification of the characteristics of gas and water distribution and its main controlling factors, it is of great importance to accurately identify the water layer, avoid the direct development of the water layer, adopt the proper production pressure differential, and carry out drainage gas production measures in time to ensure the effective development of the gas field.

The development of heavy oil reservoirs in China is of great significance to safeguard national energy security, but great challenges are faced due to the complex and heterogeneous reservoir properties. Inter-well connectivity analysis is critical to enhancing the development performance, as it is a good way to interpret fluid flow and provides a theoretical basis for injection-production optimization. Data-driven deep learning methods have been widely used in reservoir development and can be employed to develop surrogate models of injection and production and to infer inter-well connectivity. In this study, the model performance of a recurrent neural network (RNN) and its four variants were evaluated and compared in a temporal production prediction. The comparison results showed that bidirectional gated recurrent unit (Bi-GRU) is the optimal algorithm with the highest accuracy of 0.94. A surrogate model was established to simulate the inter-well connectivity of steam-assisted gravity drainage (SAGD) in the research area by utilizing the Bi-GRU algorithm. A global sensitivity analysis method, Fourier amplitude sensitivity testing (FAST), was introduced and combined with the surrogate model to explain the influence of the input variables on the output variables by quantitatively calculating the sensitivity of each variable. Quantitative results for the inter-well connectivity of SAGD were derived from the sensitivity analysis of the proposed method, which was effectively applied to typical linear patterns and five-spot patterns. Inter-well connectivity varied from 0.1 to 0.58 in test applications, and mutual corroboration with previous geological knowledge can further determine the distribution of the interlayer in the reservoir. The workflow proposed in this study provides a new direction for analyzing and inferring the inter-well connectivity of SAGD in Northeast China heavy oil reservoirs.

The rapid and accurate evaluation of oil and gas assets, specifically for new development projects, poses a significant challenge due to the various project types, limited data availability, brief periods for assessment and decision making, and constraints arising from varying contractual and taxation conditions, political stability, and societal factors. This study leverages the grading standards of the evaluation index system for new oil and gas field development projects, along with relevant mathematical theories and methods for project evaluation and optimization. We developed an asset evaluation approach for new oil and gas projects by analyzing the assets of six new oil and gas field development projects in Brazil. This analysis resulted in the grading and ranking of new projects, and we tested and demonstrated four asset optimization techniques. After a comparative analysis with conventional evaluation results, we established an oil and gas project asset optimization approach centered on the cloud model comprehensive evaluation and linear weighted ranking, exhibiting Kendall’s tau coefficient of 0.8667 with conventional methods. The findings suggest that the combination of the cloud model comprehensive evaluation method with the linear weighted ranking method can facilitate asset optimization for oil and gas field development projects, meeting the practical needs for fast selection among various new projects. Furthermore, this research offers a technical and theoretical foundation for rapid evaluation and decision making regarding new assets.

The injection of carbon dioxide (${\mathrm{CO}}_2$) into gas reservoirs has become an important way to enhance gas recovery and reduce ${\mathrm{CO}}_2$ emissions. Large discrepancies are observed when predicting natural gas compressibility factors with high ${\mathrm{CO}}_2$ content by several well-known empirical correlations. An explicit correlation is proposed to improve the prediction accuracy in the estimation of compressibility factors on condensate gases with variable ${\mathrm{CO}}_2$ contents. The analysis of the results is carried out on the basis of 202 experimental data from 9 various mixtures of natural gases. The results show that relative deviations of compressibility factors predicted by conventional empirical correlations increase with the increase in ${\mathrm{CO}}_2$ mole fraction with an average error of 8%. The average error of the new method is less than 4%. The effect of compressibility factors on the estimations of dynamic reserves is studied and the compressibility factor causes a 3% reduction in dynamic reserves estimation. The proposed correlation has fewer uncertainties and more accurate results than other correlations that involve the iterative process in calculating compressibility factors of natural gases with variable ${\mathrm{CO}}_2$ contents.