Accurately obtaining the working status of the sucker rod pumping wells is a challenging problem for oil production. Sensors at the polished rod collect working data to form surface dynamometer cards for fault diagnosis. A prevalent method for recognizing these cards is the convolutional neural network (CNN). However, this approach has two problems: an unbalanced dataset due to varying fault frequencies and similar dynamometer card shapes that complicate recognition. This leads to a low accuracy of fault diagnosis in practice, which is unsatisfactory. Therefore, this paper proposes a hybrid approach of the deep learning method and rule-based method for fault diagnosis of sucker rod pumping wells. Specifically, when the CNN model alone fails to achieve satisfactory accuracy in the working status, historical monitoring data of the relevant wells can be collected, and expert rules can assist CNN to improve diagnostic accuracy. By analyzing time series data of factors such as the maximum and minimum loads, the area of the dynamometer card, and the load difference, a knowledgebase of expert rules can be created. When performing fault diagnosis, both the dynamometer cards and related time series data are used as inputs. The dynamometer cards are used for the CNN model to diagnose, and the related time series data are used for expert rules to diagnose. The diagnostic results and the confidence levels of the two methods are obtained and compared. When the two diagnostic results conflict, the one with higher confidence is preserved. Out of the 2066 wells and 7 fault statuses analyzed in field applications, the hybrid approach demonstrated a 21.25% increase in fault diagnosis accuracy compared with using only the CNN model. Additionally, the overall accuracy rate of the hybrid approach exceeded 95%, indicating its high effectiveness in diagnosing faults in sucker rod pumping wells.

Indicator diagram is the key basis for fault diagnosis of pumping wells in oil exploitation. With the rapid development of machine learning, the fault diagnosis of indicator diagram based on deep learning has garnered increasing attention. This kind of methods train neural network models with marked samples, and then inputs images into the trained models and outputs their categories. At present, the preparation of indicator diagram sample set relies on experts' analysis of indicator diagram images one by one. However, it involves extensive manual work and manual marking is prone to errors, so the marked samples are often insufficient in quantity. In order to quickly mark a large number of indicator diagram samples, the oil well data was plotted into standardized indicator diagram, and then three feature extraction methods for indicator diagrams were proposed: feature extraction based on original vector, feature extraction based on three-dimensional pixel tensor, feature extraction based on convolutional neural network. These methods convert the indicator diagram into corresponding feature vectors, which are then clustered using the K-means clustering algorithm, enabling the corresponding indicator diagrams to be classified into different categories based on the clustering results. Using 20,000 randomly selected pieces of data from 100 pumping wells, this study clusters the sample set using the three proposed methods. The results indicated that the time consumption were 0.2, 8.3, and 0.7 h, with accuracy rates of 98%, 92%, and 95%, respectively. For indicator diagrams, the clustering method based on the original vector has outstanding performance in terms of efficiency and accuracy. This provides an automatic tool for the preparation of the pumping well fault diagnosis dataset, and its efficiency can be increased by tens of times compared with manual marking.

The influence of geological development factors such as reservoir heterogeneity needs to be comprehensively considered in the determination of oil well production control strategy. In the past, many optimization algorithms are introduced and coupled with numerical simulation for well control problems. However, these methods require a large number of simulations, and the experience of these simulations is not preserved by the algorithm. For each new reservoir, the optimization algorithm needs to start over again. To address the above problems, two reinforcement learning methods are introduced in this research. A personalized Deep Q-Network (DQN) algorithm and a personalized Soft Actor-Critic (SAC)algorithm are designed for optimal control determination of oil wells. The inputs of the algorithms are matrix of reservoir properties, including reservoir saturation, permeability, etc., which can be treated as images. The output is the oil well production strategy. A series of samples are cut from two different reservoirs to form a dataset. Each sample is a square area that takes an oil well at the center, with different permeability and saturation distribution, and different oil-water well patterns. Moreover, all samples are expanded by using image enhancement technology to further increase the number of samples and improve the coverage of the samples to the reservoir conditions. During the training process, two training strategies are investigated for each personalized algorithm. The second strategy uses 4 times more samples than the first strategy. At last, a new set of samples is designed to verify the model’s accuracy and generalization ability. Results show that both the trained DQN and SAC models can learn and store historical experience, and push appropriate control strategies based on reservoir characteristics of new oil wells. The agreement between the optimal control strategy obtained by both algorithms and the global optimal strategy obtained by the exhaustive method is more than 95%. The personalized SAC algorithm shows better performance compared to the personalized DQN algorithm. Compared to the traditional Particle Swarm Optimization (PSO), the personalized models were faster and better at capturing complex patterns and adapting to different geological conditions, making them effective for real-time decision-making and optimizing oil well production strategies. Since a large amount of historical experience has been learned and stored in the algorithm, the proposed method requires only 1 simulation for a new oil well control optimization problem, which showing the superiority in computational efficiency.

In recent years, fracturing-flooding technology has achieved a series of successful practices in the development of low-permeability oil reservoirs. However, research on the dynamic behavior of fracturing-flooding remains limited. In this paper, a dual medium model considering anisotropic characteristics is established for the target blocks. Multiple sets of conventional water injection transitions and multi-cycle fracturing-flooding operations are designed for simulation to explore the subsequent optimal operational schemes. Simulations are conducted on the optimal transitions between conventional water injection and multi-cycle fracturing-flooding schemes for different reservoir models with varying physical properties to study the dynamic behavior of fracturing-flooding in oil reservoirs with different properties. The results indicate that, for conventional water injection schemes, the optimal transition time for both the target well group and other reservoirs with different properties corresponds to a formation pressure coefficient between 1.2 and 1.3, with the optimal injection–production ratio being 1:1. From the perspective of water cut, the accumulated oil production of multi-cycle fracturing-flooding is higher than that of conventional water injection. The optimal multi-cycle fracturing-flooding schemes for both the target well group and other reservoirs with different properties are to start fracturing-flooding when the formation pressure coefficient is around 0.8 and to begin production when it reaches 1.4.