This study addresses the challenges of poor real-time performance and low accuracy in drilling condition identification by introducing an innovative intelligent recognition method. The proposed approach integrates a one-dimensional convolutional neural network (1dCNN) for local feature extraction, a bidirectional gated recurrent unit (BiGRU) to capture sequential dependencies, and a multi-head attention mechanism to emphasize critical information. This fusion enables efficient discrimination among 13 drilling conditions, including rotary drilling, slide drilling, whipstocking, and reverse whipstocking. In the model design phase, comprehensive ablation studies were conducted to evaluate the contributions of each module—1dCNN, BiGRU, self-attention, and multi-head attention—as well as their serial and parallel configurations. The performance was further optimized using the Optuna framework for automatic hyperparameter tuning. Experimental results demonstrated that the model achieved an accuracy of 96.22% on time-domain data from a single well. Additionally, in both intra-and inter-block transfer tests, the overall accuracy ranged from 94% to 97%, with each drilling condition exceeding an 80% recognition rate. Real-time testing on field data also showed a high degree of consistency with actual operational conditions. Overall, the proposed method provides a robust technical framework for real-time monitoring and optimization of drilling operations.

The formation pore pressure is an important basic parameter in the process of oil and gas drilling from well design to well completion. It is an important basis for the rational design of drilling plans and analysis of wellbore stability, and accurate calculation of formation pore pressure is an important prerequisite for ensuring drilling safety and improving drilling efficiency. In order to overcome the problems of insufficient accuracy and low calculation efficiency of traditional formation pore pressure calculation methods, this paper takes into account that both the drilling process and the formation deposition process have a certain degree of sequentiality and complex nonlinearity, so this article proposes a method to calculate formation pore pressure by combining a Long Short-Term Memory (LSTM) neural network and an error Back Propagation (BP) neural network based on drilling-logging-recording data. The LSTM layer in the neural network is used to extract the serial feature information in the multi-source data of drilling, logging, and recording, and the BP layer in the neural network is used to construct a nonlinear mapping relationship between characteristic information and formation pore pressure. The field data of an oilfield was cleaned and processed, and 18 parameters such as drilling time, weight on bit, dc-exponent, sonic time difference, and density logging were optimized through comprehensive correlation analysis and drilling experience knowledge, and the LSTM-BP formation pore pressure calculation model was carried out with training, validation and testing, and using the grid search method to analyze and optimize the 5 hyperparameters of the LSTM-BP model, including the number of LSTM layers, the number of neural units in the LSTM unit gate, the number of BP layers, the number of neurons in the BP layer, and the activation function. The mean absolute error of the best single well calculation model and the best adjacent well calculation model were 4.92 MPa and 2.34 MPa, the root mean square error were 6.65 MPa and 3.03 MPa, and the mean relative error were 4.36% and 8.31%. Finally, the LSTM-BP model is compared with the optimized traditional BP neural network model, LSTM neural network model, and Support Vector Machine (SVM) model. The results show that the accuracy of the LSTM-BP neural network model established in this paper is higher than that of the BP neural network model, LSTM neural network model, and SVM model, which show that the LSTM-BP formation pore pressure calculation model proposed in this paper has a high calculation accuracy.

Geothermal energy in hot dry rock formations is an important component of China's geothermal resources, and its development is of significant importance for achieving the "dual carbon" goals. The reservoir rocks of hot dry rock formations are mainly granites. The lithology of granites is dense and is usually developed by Enhanced Geothermal System (EGS). As the main pathways for fluid flow and heat transfer in the circulation process, both artificial and natural fractures deformation can lead to the evolution of conductivity, thereby influencing the heat extraction performance of the thermal reservoir. Existing studies on conductivity mostly focus on artificial fractures, often centered around matrix elastic deformation, without considering the impact of natural fractures damage. To reveal the effects of natural fractures damage, a high-temperature and high-pressure rock core injection and extraction multi-field coupling experimental platform is independently developed and designed. The reliability of the experimental system was analyzed and verified, corresponding experimental schemes and procedures are designed. Natural fractures were used to penetrate the rock samples, study the variations of injection and extraction differential pressure with injection flow and confining pressure at room temperature. The characteristics of natural fractures damage at high temperatures were analyzed, and the impact of natural fractures damage on the evolution of conductivity under different injection flow, temperature difference and injection modes were compared. The experiments demonstrated that injecting cold fluid resulted in a significant increase in the volume of natural fractures compared to the initial state, primarily through weak cementation failure damage. Under no confining pressure conditions, damage caused an increase in fracture aperture and length, enhancing fracture connectivity and altering fracture conductivity. Therefore, natural fractures should be considered in the design of fracturing and heat extraction schemes. The injection and extraction differential pressure increased with increasing confining pressure and injection flow, with a maximum increase of up to 0.6 MPa. During high-temperature production, the maximum changes in injection and extraction differential pressure and conductivity evolution rate reached 1.11 MPa and 26.59%, respectively. Characteristics of fracture damage are more pronounced under higher injection flows and temperature differentials. Fracture damage is more significant under intermittent injection compared to continuous injection. Grey relational analysis identified the primary controlling factor as the temperature differential, indicating that thermal stress is the main cause of additional conductivity evolution due to fracture damage. This study highlights the necessity of analyzing natural fractures damage in the long-term production process of hot dry rock formations, providing valuable guidance for engineering field construction.

This study introduces a three-dimensional supervoxel segmentation method to accurately separate solid and fluid phases in X-ray images of porous materials, with applications in energy research. Compared with intelligent segmentation algorithms requiring model training, the proposed method operates as a ready-to-use solution with significantly enhanced efficiency. When benchmarked against conventional approaches such as watershed transformation, our technique demonstrates superior segmentation accuracy. Tested on porous rock and gas diffusion layers under varying wettability, it accurately quantifies fluid saturation, interfacial area, curvature, and contact angles-key parameters for enhanced oil recovery, CO₂ storage, and hydrogen fuel cells. The proposed three-dimensional segmentation method is noise-resistant and annotation-free, improving both the accuracy and efficiency of segmenting diverse micro-structural material datasets and providing reliable measurements of their geometric characteristics.

Water-rock reactions within the reservoir during karst-based geothermal exploitation can lead to variation of porosity and permeability, affecting the system's thermal performance. A multi-field coupled thermal-hydraulic-chemical model considering the pore changes was developed to investigate the mechanism of water-rock reaction on the evolution of pores and permeability during the production process of karst-based geothermal reservoirs. The mechanism of water-rock reaction on reservoir pore-permeability evolution under thermal-hydraulic-chemical coupling is revealed, and the evolutionary characteristics of pore-permeability parameters and their evolutionary impact on the system heat extraction performance are investigated. The results show that under undersaturated injection conditions, the concentration and porosity distribution present a ring shape, which is caused by dissolution reactions occurring in the injection wells and swept front regions, and precipitation reactions occurring in the intermediate regions. After 30 years of production, the porosity and permeability at the injection wells increased by 19.3% and 73.6%, respectively, and the porosity and permeability at the production wells decreased by 0.11% and 0.32%, respectively. The injection-production differential pressure of the case considering pore deformation was 21.7% lower than that of the case without considering pore deformation. This study is instructive for the accurate prediction of carbonate karst-based reservoir production and development program optimization.

The quality of cementing is crucial for the production efficiency and lifespan of oil and gas wells. Currently, the most widely used method is acoustic amplitude variable density logging for evaluation. However, the interpretation process is complex, and decisions related to major risks need to be made based on the results of cementing interpretation. Therefore, the evaluation of cementing quality must be undertaken by experienced experts, which is time-consuming and labor-intensive. In order to improve the efficiency of cementing interpretation, we used convolutional neural networks such as VGG and ResNet to automatically interpret cementing quality, but the accuracy was insufficient. Therefore, we proposes a method of parallel connection between multi-layer perceptions and convolutional neural networks (MLP-CNN), where acoustic amplitude data is input into multi-layer perceptions and variable density logging images are input into convolutional neural networks; We modifies the structure of convolutional neural networks by setting convolutional kernels of different sizes to extract information at different scales for features with varying density maps, such as the thickness, brightness, and shape of stripes. We used 9000 data from the Fuyuan block of the Tarim Oilfield for training and validation. The results showed that compared to traditional convolutional networks such as VGG and ResNet, the MLP and CNN parallel networks effectively improved the accuracy of cementing quality recognition, with an evaluation accuracy of 90%. Furthermore, compared to a single scale convolutional kernel, the convolutional neural network algorithm with multiple convolutional kernels of different sizes is more suitable for extracting features from variable density cementing images. We modified the structure of the convolutional neural network and established an MLP-CNN neural network with three convolutional kernels of different sizes, which improved the accuracy by 5% compared to the MLPCNN model with a single convolutional kernel; meanwhile, we compared the time complexity and spatial complexity of seven networks. The findings revealed that the MLP-CNN parallel network efficiently mitigates a substantial number of ineffective convolutions, thereby reducing model computational costs and enhancing computational efficiency. Finally, in order to test the transferability of the model, we used 60000 data from the Manshen and Yueman blocks of the Tarim Oilfield for testing, and the evaluation accuracy reached 89%, indicating a satisfactory migration effect and robust performance of the model.

As global oil exploration ventures into deeper and more complex territories, drilling bit wear and damage have emerged as significant constraints on drilling efficiency and safety. Despite the publication of official bit wear evaluation standards by the International Association of Drill Contractors (IADC), the current lack of quantitative and scientific evaluation techniques means that bit wear assessments rely heavily on engineers' experience. Consequently, forming a standardized database of drilling bit information to underpin the mechanisms of bit wear and facilitate optimal design remains challenging. Therefore, an efficient and quantitative evaluation of bit wear is crucial for optimizing bit performance and improving penetration efficiency.

This paper introduces an automatic standard workflow for the quantitative evaluation of bit wear and the design of a comprehensive bit information database. Initially, a method for acquiring images of worn bits at the drilling site was developed. Subsequently, the wear classification and grading models based on computer vision were established to determine bit status. The wear classification model focuses on the positioning and classification of bit cutters, while the wear grading model quantifies the extent of bit wear. After that, the automatic evaluation method of the bit wear is realized. Additionally, bit wear evaluation software was designed, integrating all necessary functions to assess bit wear in accordance with IADC standards. Finally, a drilling bit database was created by integrating bit wear data, logging data, mud-logging data, and basic drilling bit data.

This workflow represents a novel approach to collecting and analyzing drilling bit information at drilling sites. It holds potential to facilitate the creation of a large-scale information database for the entire lifecycle of drilling bits, marking the inception of intelligent analysis, design, and manufacture of drilling bits, thereby enhancing performance in challenging drilling conditions.

Digital rock analysis is a promising approach for visualizing geological microstructures and understanding transport mechanisms for underground geo-energy resources exploitation. Accurate image reconstruction methods are vital for capturing the diverse features and variability in digital rock samples. Stable diffusion, a cutting-edge artificial intelligence model, has revolutionized computer vision by creating realistic images. However, its application in digital rock analysis is still emerging. This study explores the applications of stable diffusion in digital rock analysis, including enhancing image resolution, improving quality with denoising and deblurring, segmenting images, filling missing sections, extending images with outpainting, and reconstructing three-dimensional rocks from two-dimensional images. The powerful image generation capability of diffusion models shed light on digital rock analysis, showing potential in filling missing parts of rock images, lithologic discrimination, and generating network parameters. In addition, limitations in existing stable diffusion models are also discussed, including the lack of real digital rock images, and not being able to fully understand the mechanisms behind physical processes. Therefore, it is suggested to develop new models tailored to digital rock images for further progress. In sum, the integration of stable diffusion into digital core analysis presents immense research opportunities and holds the potential to transform the field, ushering in groundbreaking advances.

Accurate prediction of the rate of penetration (ROP) is significant for drilling optimization. While the intelligent ROP prediction model based on fully connected neural networks (FNN) outperforms traditional ROP equations and machine learning algorithms, its lack of interpretability undermines its credibility. This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit (ReLU) activation function. By leveraging the derivative of the ReLU function, the FNN function calculation process is transformed into vector operations. The FNN model is linearly characterized through further simplification, enabling its interpretation and analysis. The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield. The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well. The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity. In the well sections with similar drilling data, averaging the weight parameters enables linear characterization of the FNN ROP prediction model, leading to the establishment of a corresponding linear representation equation. Furthermore, the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section. The established linear characterization equation exhibits high precision, strong stability, and adaptability through the application and validation across multiple well sections.

Many scholars have focused on applying machine learning models in bottom hole pressure (BHP) prediction. However, the complex and uncertain conditions in deep wells make it difficult to capture spatial and temporal correlations of measurement while drilling (MWD) data with traditional intelligent models. In this work, we develop a novel hybrid neural network, which integrates the Convolution Neural Network (CNN) and the Gate Recurrent Unit (GRU) for predicting BHP fluctuations more accurately. The CNN structure is used to analyze spatial local dependency patterns and the GRU structure is used to discover depth variation trends of MWD data. To further improve the prediction accuracy, we explore two types of GRU-based structure: skip-GRU and attention-GRU, which can capture more long-term potential periodic correlation in drilling data. Then, the different model structures tuned by the Bayesian optimization (BO) algorithm are compared and analyzed. Results indicate that the hybrid models can extract spatial-temporal information of data effectively and predict more accurately than random forests, extreme gradient boosting, back propagation neural network, CNN and GRU. The CNN-attention-GRU model with BO algorithm shows great superiority in prediction accuracy and robustness due to the hybrid network structure and attention mechanism, having the lowest mean absolute percentage error of 0.025%. This study provides a reference for solving the problem of extracting spatial and temporal characteristics and guidance for managed pressure drilling in complex formations.